2022-10-25 15:11:12 +08:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"#新增加的两行\n",
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"from pylab import mpl\n",
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"\n",
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"# 设置显示中文字体\n",
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2022-10-31 14:20:50 +08:00
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"\n",
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2022-10-25 15:11:12 +08:00
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"mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
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"\n",
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"mpl.rcParams[\"axes.unicode_minus\"] = False"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [
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{
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"data": {
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"text/plain": " 日期 企业名称 地址 省份 经度 纬度 烟囱高度（m） \\\n0 2018-10-01 浙江秀舟热电有限公司 嘉兴市南湖区凤桥镇 浙江省 120°51′5.54″ 30°39′14.76″ 80 \n1 2018-10-02 浙江秀舟热电有限公司 嘉兴市南湖区凤桥镇 浙江省 120°51′5.54″ 30°39′14.76″ 80 \n2 2018-10-03 浙江秀舟热电有限公司 嘉兴市南湖区凤桥镇 浙江省 120°51′5.54″ 30°39′14.76″ 80 \n3 2018-10-04 浙江秀舟热电有限公司 嘉兴市南湖区凤桥镇 浙江省 120°51′5.54″ 30°39′14.76″ 80 \n4 2018-10-05 浙江秀舟热电有限公司 嘉兴市南湖区凤桥镇 浙江省 120°51′5.54″ 30°39′14.76″ 80 \n\n 脱硝工艺 脱硝剂名称 脱硝设备数量 ... 供热量（吉焦） 产渣量（吨） 机组运行时间（小时） 硫分（%） 脱硫副产品产量（吨） \\\n0 SNCR SCR 氨水 3 ... 6536.83 NaN 24.0 0.51 NaN \n1 SNCR SCR 氨水 3 ... 2484.64 NaN 24.0 0.51 NaN \n2 SNCR SCR 氨水 3 ... 3020.83 NaN 24.0 0.51 NaN \n3 SNCR SCR 氨水 3 ... 5599.23 NaN 24.0 0.51 72.52 \n4 SNCR SCR 氨水 3 ... 4702.65 NaN 24.0 0.51 NaN \n\n 脱硫剂使用量（吨） 脱硫设施运行时间（小时） 脱硝还原剂消耗量（吨） 脱硝运行时间（小时） 燃料消耗量（吨） \n0 5.06 24.0 2.98 24.0 323 \n1 5.04 24.0 2.97 24.0 218 \n2 5.04 24.0 2.95 24.0 212 \n3 5.03 24.0 2.98 24.0 223 \n4 5.06 24.0 3.01 24.0 243 \n\n[5 rows x 44 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>日期</th>\n <th>企业名称</th>\n <th>地址</th>\n <th>省份</th>\n <th>经度</th>\n <th>纬度</th>\n <th>烟囱高度（m）</th>\n <th>脱硝工艺</th>\n <th>脱硝剂名称</th>\n <th>脱硝设备数量</th>\n <th>...</th>\n <th>供热量（吉焦）</th>\n <th>产渣量（吨）</th>\n <th>机组运行时间（小时）</th>\n <th>硫分（%）</th>\n <th>脱硫副产品产量（吨）</th>\n <th>脱硫剂使用量（吨）</th>\n <th>脱硫设施运行时间（小时）</th>\n <th>脱硝还原剂消耗量（吨）</th>\n <th>脱硝运行时间（小时）</th>\n <th>燃料消耗量（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>浙江秀舟热电有限公司</td>\n <td>嘉兴市南湖区凤桥镇</td>\n <td>浙江省</td>\n <td>120°51′5.54″</td>\n <td>30°39′14.76″</td>\n <td>80</td>\n <td>SNCR SCR</td>\n <td>氨水</td>\n <td>3</td>\n <td>...</td>\n <td>6536.83</td>\n <td>NaN</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>NaN</td>\n <td>5.06</td>\n <td>24.0</td>\n <td>2.98</td>\n <td>24.0</td>\n <td>323</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>浙江秀舟热电有限公司</td>\n <td>嘉兴市南湖区凤桥镇</td>\n <td>浙江省</td>\n <td>120°51′5.54″</td>\n <td>30°39′14.76″</td>\n <td>80</td>\n <td>SNCR SCR</td>\n <td>氨水</td>\n <td>3</td>\n <td>...</td>\n <td>2484.64</td>\n <td>NaN</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>NaN</td>\n <td>5.04</td>\n <td>24.0</td>\n <td>2.97</td>\n <td>24.0</td>\n <td>218</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>浙江秀舟热电有限公司</td>\n <td>嘉兴市南湖区凤桥镇</td>\n <td>浙江省</td>\n <td>120°51′5.54″</td>\n <td>30°39′14.76″</td>\n <td>80</td>\n <td>SNCR SCR</td>\n <td>氨水</td>\n <td>3</td>\n <td>...</td>\n <td>3020.83</td>\n <td>NaN</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>NaN</td>\n <td>5.04</td>\n <td>24.0</td>\n <td>2.95</td>\n <td>24.0</td>\n <td>212</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>浙江秀舟热电有限公司</td>\n <td>嘉兴市南湖区凤桥镇</td>\n <td>浙江省</td>\n <td>120°51′5.54″</td>\n <td>30°39′14.76″</td>\n <td>80</td>\n <td>SNCR SCR</td>\n <td>氨水</td>\n <td>3</td>\n <td>...</td>\n <td>5599.23</td>\n <td>NaN</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>72.52</td>\n <td>5.03</td>\n <td>24.0</td>\n <td>2.98</td>\n <td>24.0</td>\n <td>223</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>浙江秀舟热电有限公司</td>\n <td>嘉兴市南湖区凤桥镇</td>\n <td>浙江省</td>\n <td>120°51′5.54″</td>\n <td>30°39′14.76″</td>\n <td>80</td>\n <td>SNCR SCR</td>\n <td>氨水</td>\n <td>3</td>\n <td>...</td>\n <td>4702.65</td>\n <td>NaN</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>NaN</td>\n <td>5.06</td>\n <td>24.0</td>\n <td>3.01</td>\n <td>24.0</td>\n <td>243</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 44 columns</p>\n</div>"
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"zjxz_daily = pd.read_excel('./data/机器学习样表.xlsx', sheet_name=0, header=[0, 1])\n",
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"old_cols = zjxz_daily.columns\n",
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"new_cols = [x[0].strip() if 'Unnamed' in x[1] else x[0] + '_' + x[1] for x in old_cols]\n",
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"zjxz_daily.columns = new_cols\n",
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"zjxz_daily.head()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [],
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"source": [
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"zjxz_daily_data = zjxz_daily[['日期', '企业名称', '发电量（千瓦时）', '供热量（吉焦）', '燃料消耗量（吨）']].copy()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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2022-10-31 14:20:50 +08:00
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"execution_count": 49,
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"outputs": [
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{
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"data": {
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"text/plain": " 日期 企业名称 发电量（千瓦时） 供热量（吉焦） 燃料消耗量（吨）\n0 2018-10-01 浙江秀舟热电有限公司 156796.00 6536.83 323\n1 2018-10-02 浙江秀舟热电有限公司 133984.00 2484.64 218\n2 2018-10-03 浙江秀舟热电有限公司 134023.00 3020.83 212\n3 2018-10-04 浙江秀舟热电有限公司 124765.00 5599.23 223\n4 2018-10-05 浙江秀舟热电有限公司 134414.00 4702.65 243\n... ... ... ... ... ...\n1173 2022-01-22 浙江秀舟热电有限公司 52.24 12472.00 822\n1174 2022-01-23 浙江秀舟热电有限公司 51.36 12051.00 790\n1175 2022-01-24 浙江秀舟热电有限公司 51.12 11276.00 751\n1176 2022-01-25 浙江秀舟热电有限公司 49.32 11007.00 672\n1177 2022-01-26 浙江秀舟热电有限公司 29.64 8132.00 484\n\n[1178 rows x 5 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>日期</th>\n <th>企业名称</th>\n <th>发电量（千瓦时）</th>\n <th>供热量（吉焦）</th>\n <th>燃料消耗量（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>浙江秀舟热电有限公司</td>\n <td>156796.00</td>\n <td>6536.83</td>\n <td>323</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>浙江秀舟热电有限公司</td>\n <td>133984.00</td>\n <td>2484.64</td>\n <td>218</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>浙江秀舟热电有限公司</td>\n <td>134023.00</td>\n <td>3020.83</td>\n <td>212</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>浙江秀舟热电有限公司</td>\n <td>124765.00</td>\n <td>5599.23</td>\n <td>223</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>浙江秀舟热电有限公司</td>\n <td>134414.00</td>\n <td>4702.65</td>\n <td>243</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1173</th>\n <td>2022-01-22</td>\n <td>浙江秀舟热电有限公司</td>\n <td>52.24</td>\n <td>12472.00</td>\n <td>822</td>\n </tr>\n <tr>\n <th>1174</th>\n <td>2022-01-23</td>\n <td>浙江秀舟热电有限公司</td>\n <td>51.36</td>\n <td>12051.00</td>\n <td>790</td>\n </tr>\n <tr>\n <th>1175</th>\n <td>2022-01-24</td>\n <td>浙江秀舟热电有限公司</td>\n <td>51.12</td>\n <td>11276.00</td>\n <td>751</td>\n </tr>\n <tr>\n <th>1176</th>\n <td>2022-01-25</td>\n <td>浙江秀舟热电有限公司</td>\n <td>49.32</td>\n <td>11007.00</td>\n <td>672</td>\n </tr>\n <tr>\n <th>1177</th>\n <td>2022-01-26</td>\n <td>浙江秀舟热电有限公司</td>\n <td>29.64</td>\n <td>8132.00</td>\n <td>484</td>\n </tr>\n </tbody>\n</table>\n<p>1178 rows × 5 columns</p>\n</div>"
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},
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"execution_count": 49,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"zjxz_daily_data"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"outputs": [
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{
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"data": {
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"text/plain": " days 发电量（千瓦时） 供热量（吉焦） 机组运行时间（小时） 硫分（%） 脱硫剂使用量（吨） \\\n0 2018-10-01 156796.00 6536.83 24.0 0.51 5.06 \n1 2018-10-02 133984.00 2484.64 24.0 0.51 5.04 \n2 2018-10-03 134023.00 3020.83 24.0 0.51 5.04 \n3 2018-10-04 124765.00 5599.23 24.0 0.51 5.03 \n4 2018-10-05 134414.00 4702.65 24.0 0.51 5.06 \n... ... ... ... ... ... ... \n1082 2022-01-22 52.24 12472.00 24.0 0.59 8.46 \n1083 2022-01-23 51.36 12051.00 24.0 0.59 8.46 \n1084 2022-01-24 51.12 11276.00 24.0 0.59 8.43 \n1085 2022-01-25 49.32 11007.00 24.0 0.59 8.43 \n1086 2022-01-26 29.64 8132.00 24.0 0.59 8.44 \n\n 脱硫设施运行时间（小时） 脱硝还原剂消耗量（吨） 脱硝运行时间（小时） 燃料消耗量（吨） ... cSO2 \\\n0 24.0 2.98 24.0 323 ... 2.148473e+07 \n1 24.0 2.97 24.0 218 ... 1.587722e+07 \n2 24.0 2.95 24.0 212 ... 2.829086e+07 \n3 24.0 2.98 24.0 223 ... 1.030569e+07 \n4 24.0 3.01 24.0 243 ... 1.830254e+06 \n... ... ... ... ... ... ... \n1082 24.0 4.56 24.0 822 ... 3.626034e+06 \n1083 24.0 4.58 24.0 790 ... 4.074431e+06 \n1084 24.0 4.57 24.0 751 ... 4.928013e+06 \n1085 24.0 4.56 24.0 672 ... 4.584759e+06 \n1086 24.0 4.57 24.0 484 ... 5.701371e+06 \n\n cO2 csmoke flow rNOx rO2 \\\n0 3.745944e+07 6.519466e+05 162345.192917 28.981417 9.900000 \n1 2.832146e+07 3.656575e+05 140175.330833 22.220750 9.400000 \n2 3.174159e+07 5.181773e+05 154686.184167 24.816708 8.550000 \n3 2.511504e+07 2.299438e+06 120345.545833 21.875125 10.202083 \n4 4.106346e+07 6.230433e+06 162533.103542 25.605917 11.497917 \n... ... ... ... ... ... \n1082 4.149625e+07 6.512159e+06 218349.604167 2.603662 7.921417 \n1083 4.422277e+07 6.203422e+06 210121.608333 6.638867 8.756333 \n1084 4.655727e+07 6.101480e+06 211378.329167 12.277371 9.110167 \n1085 7.959093e+07 6.326125e+06 240801.208333 5.684492 13.636042 \n1086 9.866431e+07 6.632631e+06 263197.579167 1.672292 15.621583 \n\n temp rSO2 rsmoke day_of_year \n0 51.250000 5.581667 0.209167 273 \n1 50.679167 4.364167 0.190417 274 \n2 52.808333 7.580000 0.139583 275 \n3 48.854167 2.808958 0.893333 276 \n4 45.783333 0.393333 2.141875 277 \n... ... ... ... ... \n1082 55.441542 0.690904 1.242762 21 \n1083 54.574333 0.810112 1.230213 22 \n1084 53.031042 0.973396 1.203483 23 \n1085 42.908458 0.807300 1.098229 24 \n1086 36.412917 0.904429 1.050008 25 \n\n[1087 rows x 165 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>days</th>\n <th>发电量（千瓦时）</th>\n <th>供热量（吉焦）</th>\n <th>机组运行时间（小时）</th>\n <th>硫分（%）</th>\n <th>脱硫剂使用量（吨）</th>\n <th>脱硫设施运行时间（小时）</th>\n <th>脱硝还原剂消耗量（吨）</th>\n <th>脱硝运行时间（小时）</th>\n <th>燃料消耗量（吨）</th>\n <th>...</th>\n <th>cSO2</th>\n <th>cO2</th>\n <th>csmoke</th>\n <th>flow</th>\n <th>rNOx</th>\n <th>rO2</th>\n <th>temp</th>\n <th>rSO2</th>\n <th>rsmoke</th>\n <th>day_of_year</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>156796.00</td>\n <td>6536.83</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>5.06</td>\n <td>24.0</td>\n <td>2.98</td>\n <td>24.0</td>\n <td>323</td>\n <td>...</td>\n <td>2.148473e+07</td>\n <td>3.745944e+07</td>\n <td>6.519466e+05</td>\n <td>162345.192917</td>\n <td>28.981417</td>\n <td>9.900000</td>\n <td>51.250000</td>\n <td>5.581667</td>\n <td>0.209167</td>\n <td>273</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>133984.00</td>\n <td>2484.64</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>5.04</td>\n <td>24.0</td>\n <td>2.97</td>\n <td>24.0</td>\n <td>218</td>\n <td>...</td>\n <td>1.587722e+07</td>\n <td>2.832146e+07</td>\n <td>3.656575e+05</td>\n <td>140175.330833</td>\n <td>22.220750</td>\n <td>9.400000</td>\n <td>50.679167</td>\n <td>4.364167</td>\n <td>0.190417</td>\n <td>274</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>134023.00</td>\n <td>3020.83</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>5.04</td>\n <td>24.0</td>\n <td>2.95</td>\n <td>24.0</td>\n <td>212</td>\n <td>...</td>\n <td>2.829086e+07</td>\n <td>3.174159e+07</td>\n <td>5.181773e+05</td>\n <td>154686.184167</td>\n <td>24.816708</td>\n <td>8.550000</td>\n <td>52.808333</td>\n <td>7.580000</td>\n <td>0.139583</td>\n <td>275</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>124765.00</td>\n <td>5599.23</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>5.03</td>\n <td>24.0</td>\n <td>2.98</td>\n <td>24.0</td>\n <td>223</td>\n <td>...</td>\n <td>1.030569e+07</td>\n <td>2.511504e+07</td>\n <td>2.299438e+06</td>\n <td>120345.545833</td>\n <td>21.875125</td>\n <td>10.202083</td>\n <td>48.854167</td>\n <td>2.808958</td>\n <td>0.893333</td>\n <td>276</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>134414.00</td>\n <td>4702.65</td>\n <td>24.0</td>\n <td>0.51</td>\n <td>5.06</td>\n <td>24.0</td>\n <td>3.01</td>\n <td>24.0</td>\n <td>243</td>\n <td>...</td>\n <td>1.830254e+06</td>\n <td>4.106346e+07</td>\n <td>6.230433e+06</td>\n <td>162533.103542</td>\n <td>25.605917</td>\n <td>11.497917</td>\n <td>45.783333</td>\n <td>0.393333</td>\n <td>2.141875</td>\n <td>277</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>
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"source": [
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"zjxz_emiss_data = pd.read_csv('data/train_data.csv')\n",
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"zjxz_emiss_data"
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"execution_count": 60,
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"outputs": [
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{
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"data": {
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"text/plain": " days 发电量（千瓦时） 供热量（吉焦） 燃料消耗量（吨） flow rNOx \\\n0 2018-10-01 156796.00 6536.83 323 162345.192917 28.981417 \n1 2018-10-02 133984.00 2484.64 218 140175.330833 22.220750 \n2 2018-10-03 134023.00 3020.83 212 154686.184167 24.816708 \n3 2018-10-04 124765.00 5599.23 223 120345.545833 21.875125 \n4 2018-10-05 134414.00 4702.65 243 162533.103542 25.605917 \n... ... ... ... ... ... ... \n1082 2022-01-22 52.24 12472.00 822 218349.604167 2.603662 \n1083 2022-01-23 51.36 12051.00 790 210121.608333 6.638867 \n1084 2022-01-24 51.12 11276.00 751 211378.329167 12.277371 \n1085 2022-01-25 49.32 11007.00 672 240801.208333 5.684492 \n1086 2022-01-26 29.64 8132.00 484 263197.579167 1.672292 \n\n rO2 temp rSO2 rsmoke \n0 9.900000 51.250000 5.581667 0.209167 \n1 9.400000 50.679167 4.364167 0.190417 \n2 8.550000 52.808333 7.580000 0.139583 \n3 10.202083 48.854167 2.808958 0.893333 \n4 11.497917 45.783333 0.393333 2.141875 \n... ... ... ... ... \n1082 7.921417 55.441542 0.690904 1.242762 \n1083 8.756333 54.574333 0.810112 1.230213 \n1084 9.110167 53.031042 0.973396 1.203483 \n1085 13.636042 42.908458 0.807300 1.098229 \n1086 15.621583 36.412917 0.904429 1.050008 \n\n[1087 rows x 10 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>days</th>\n <th>发电量（千瓦时）</th>\n <th>供热量（吉焦）</th>\n <th>燃料消耗量（吨）</th>\n <th>flow</th>\n <th>rNOx</th>\n <th>rO2</th>\n <th>temp</th>\n <th>rSO2</th>\n <th>rsmoke</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>156796.00</td>\n <td>6536.83</td>\n <td>323</td>\n <td>162345.192917</td>\n <td>28.981417</td>\n <td>9.900000</td>\n <td>51.250000</td>\n <td>5.581667</td>\n <td>0.209167</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>133984.00</td>\n <td>2484.64</td>\n <td>218</td>\n <td>140175.330833</td>\n <td>22.220750</td>\n <td>9.400000</td>\n <td>50.679167</td>\n <td>4.364167</td>\n <td>0.190417</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>134023.00</td>\n <td>3020.83</td>\n <td>212</td>\n <td>154686.184167</td>\n <td>24.816708</td>\n <td>8.550000</td>\n <td>52.808333</td>\n <td>7.580000</td>\n <td>0.139583</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>124765.00</td>\n <td>5599.23</td>\n <td>223</td>\n <td>120345.545833</td>\n <td>21.875125</td>\n <td>10.202083</td>\n <td>48.854167</td>\n <td>2.808958</td>\n <td>0.893333</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>134414.00</td>\n <td>4702.65</td>\n <td>243</td>\n <td>162533.103542</td>\n <td>25.605917</td>\n <td>11.497917</td>\n <td>45.783333</td>\n <td>0.393333</td>\n <td>2.141875</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1082</th>\n <td>2022-01-22</td>\n <td>52.24</td>\n <td>12472.00</td>\n <td>822</td>\n <td>218349.604167</td>\n <td>2.603662</td>\n <td>7.921417</td>\n <td>55.441542</td>\n <td>0.690904</td>\n <td>1.242762</td>\n </tr>\n <tr>\n <th>1083</th>\n <td>2022-01-23</td>\n <td>51.36</td>\n <td>12051.00</td>\n <td>790</td>\n <td>210121.608333</td>\n <td>6.638867</td>\n <td>8.756333</td>\n <td>54.574333</td>\n <td>0.810112</td>\n <td>1.230213</td>\n </tr>\n <tr>\n <th>1084</th>\n <td>2022-01-24</td>\n <td>51.12</td>\n <td>11276.00</td>\n <td>751</td>\n <td>211378.329167</td>\n <td>12.277371</td>\n <td>9.110167</td>\n <td>53.031042</td>\n <td>0.973396</td>\n <td>1.203483</td>\n </tr>\n <tr>\n <th>1085</th>\n <td>2022-01-25</td>\n <td>49.32</td>\n <td>11007.00</td>\n <td>672</td>\n <td>240801.208333</td>\n <td>5.684492</td>\n <td>13.636042</td>\n <td>42.908458</td>\n <td>0.807300</td>\n <td>1.098229</td>\n </tr>\n <tr>\n <th>1086</th>\n <td>2022-01-26</td>\n <td>29.64</td>\n <td>8132.00</td>\n <td>484</td>\n <td>263197.579167</td>\n <td>1.672292</td>\n <td>15.621583</td>\n <td>36.412917</td>\n <td>0.904429</td>\n <td>1.050008</td>\n </tr>\n </tbody>\n</table>\n<p>1087 rows × 10 columns</p>\n</div>"
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"execution_count": 60,
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"zjxz_save_cols = zjxz_emiss_data.columns[:3].tolist() + ['燃料消耗量（吨）'] + zjxz_emiss_data.columns[-7:-1].tolist()\n",
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"zjxz_save_data = zjxz_emiss_data[zjxz_save_cols].copy()\n",
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"zjxz_save_data"
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],
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"metadata": {
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"collapsed": false,
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"execution_count": 61,
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"outputs": [],
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"source": [
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"for col in ['rNOx', 'rSO2', 'rsmoke']:\n",
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" zjxz_save_data[col] = zjxz_save_data[col]*101800*273 / (101325 * (273 + zjxz_save_data.temp))"
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"execution_count": 62,
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"outputs": [
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{
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"data": {
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"text/plain": " days 发电量（千瓦时） 供热量（吉焦） 燃料消耗量（吨） flow rNOx \\\n0 2018-10-01 156796.00 6536.83 323 162345.192917 24.515087 \n1 2018-10-02 133984.00 2484.64 218 140175.330833 18.829456 \n2 2018-10-03 134023.00 3020.83 212 154686.184167 20.891797 \n3 2018-10-04 124765.00 5599.23 223 120345.545833 18.641687 \n4 2018-10-05 134414.00 4702.65 243 162533.103542 22.031219 \n... ... ... ... ... ... ... \n1082 2022-01-22 52.24 12472.00 822 218349.604167 2.174305 \n1083 2022-01-23 51.36 12051.00 790 210121.608333 5.558760 \n1084 2022-01-24 51.12 11276.00 751 211378.329167 10.328571 \n1085 2022-01-25 49.32 11007.00 672 240801.208333 4.935421 \n1086 2022-01-26 29.64 8132.00 484 263197.579167 1.482407 \n\n rO2 temp rSO2 rsmoke \n0 9.900000 51.250000 4.721475 0.176932 \n1 9.400000 50.679167 3.698115 0.161356 \n2 8.550000 52.808333 6.381178 0.117507 \n3 10.202083 48.854167 2.393757 0.761287 \n4 11.497917 45.783333 0.338422 1.842860 \n... ... ... ... ... \n1082 7.921417 55.441542 0.576970 1.037824 \n1083 8.756333 54.574333 0.678312 1.030064 \n1084 9.110167 53.031042 0.818888 1.012453 \n1085 13.636042 42.908458 0.700918 0.953511 \n1086 15.621583 36.412917 0.801733 0.930782 \n\n[1087 rows x 10 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>days</th>\n <th>发电量（千瓦时）</th>\n <th>供热量（吉焦）</th>\n <th>燃料消耗量（吨）</th>\n <th>flow</th>\n <th>rNOx</th>\n <th>rO2</th>\n <th>temp</th>\n <th>rSO2</th>\n <th>rsmoke</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>156796.00</td>\n <td>6536.83</td>\n <td>323</td>\n <td>162345.192917</td>\n <td>24.515087</td>\n <td>9.900000</td>\n <td>51.250000</td>\n <td>4.721475</td>\n <td>0.176932</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>133984.00</td>\n <td>2484.64</td>\n <td>218</td>\n <td>140175.330833</td>\n <td>18.829456</td>\n <td>9.400000</td>\n <td>50.679167</td>\n <td>3.698115</td>\n <td>0.161356</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>134023.00</td>\n <td>3020.83</td>\n <td>212</td>\n <td>154686.184167</td>\n <td>20.891797</td>\n <td>8.550000</td>\n <td>52.808333</td>\n <td>6.381178</td>\n <td>0.117507</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>124765.00</td>\n <td>5599.23</td>\n <td>223</td>\n <td>120345.545833</td>\n <td>18.641687</td>\n <td>10.202083</td>\n <td>48.854167</td>\n <td>2.393757</td>\n <td>0.761287</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>134414.00</td>\n <td>4702.65</td>\n <td>243</td>\n <td>162533.103542</td>\n <td>22.031219</td>\n <td>11.497917</td>\n <td>45.783333</td>\n <td>0.338422</td>\n <td>1.842860</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1082</th>\n <td>2022-01-22</td>\n <td>52.24</td>\n <td>12472.00</td>\n <td>822</td>\n <td>218349.604167</td>\n <td>2.174305</td>\n <td>7.921417</td>\n <td>55.441542</td>\n <td>0.576970</td>\n <td>1.037824</td>\n </tr>\n <tr>\n <th>1083</th>\n <td>2022-01-23</td>\n <td>51.36</td>\n <td>12051.00</td>\n <td>790</td>\n <td>210121.608333</td>\n <td>5.558760</td>\n <td>8.756333</td>\n <td>54.574333</td>\n <td>0.678312</td>\n <td>1.030064</td>\n </tr>\n <tr>\n <th>1084</th>\n <td>2022-01-24</td>\n <td>51.12</td>\n <td>11276.00</td>\n <td>751</td>\n <td>211378.329167</td>\n <td>10.328571</td>\n <td>9.110167</td>\n <td>53.031042</td>\n <td>0.818888</td>\n <td>1.012453</td>\n </tr>\n <tr>\n <th>1085</th>\n <td>2022-01-25</td>\n <td>49.32</td>\n <td>11007.00</td>\n <td>672</td>\n <td>240801.208333</td>\n <td>4.935421</td>\n <td>13.636042</td>\n <td>42.908458</td>\n <td>0.700918</td>\n <td>0.953511</td>\n </tr>\n <tr>\n <th>1086</th>\n <td>2022-01-26</td>\n <td>29.64</td>\n <td>8132.00</td>\n <td>484</td>\n <td>263197.579167</td>\n <td>1.482407</td>\n <td>15.621583</td>\n <td>36.412917</td>\n <td>0.801733</td>\n <td>0.930782</td>\n </tr>\n </tbody>\n</table>\n<p>1087 rows × 10 columns</p>\n</div>"
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},
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"execution_count": 62,
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"metadata": {},
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"output_type": "execute_result"
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"cell_type": "code",
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"execution_count": 4,
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{
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"data": {
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"text/plain": "Index(['企业名称', '地址', '省份', '经度', '纬度', '烟囱高度(m)', '机组数量', '单机容量（MW）', '生产设备类型',\n '锅炉额定蒸发量 t/h ', '汽轮机类型', '压力参数', '冷却方式', '脱硝工艺', '脱硫工艺', '除尘工艺',\n 'Unnamed: 16', '日期', '机组编号', '投产日期', '燃煤干燥无灰基挥发分vda（%）',\n '入炉煤低位发热量（GJ/t）', '入炉煤消耗量发电（吨）', '入炉煤消耗量供热（吨）', '脱硝还原剂使用量a侧（吨）',\n '脱硝还原剂使用量b侧（吨）', '脱硝设施运行时间a侧（小时）', '脱硝设施运行时间b侧（小时）', '发电量', '供热量(吉焦)',\n '机组运行时间', '硫分（%）', '脱硫副产品产量（吨）', '脱硫剂使用量（吨）', '脱硫设施运行时间（小时）',\n '脱硝还原剂消耗量（吨）', '燃料消耗量（吨）', '石灰石总量（吨）', 'Unnamed: 38', 'Unnamed: 39',\n '日期.1', '机组编号.1', '投产日期.1', '燃煤干燥无灰基挥发分vda（%）.1', '入炉煤低位发热量（GJ/t）.1',\n '入炉煤消耗量发电（吨）.1', '入炉煤消耗量供热（吨）.1', '脱硝还原剂使用量a侧（吨）.1', '脱硝还原剂使用量b侧（吨）.1',\n '脱硝设施运行时间a侧（小时）.1', '脱硝设施运行时间b侧（小时）.1', '发电量（万千瓦时）', '供热量（吉焦）',\n '机组运行时间（小时）', '硫分（%）.1', '脱硫副产品产量（吨）.1', '脱硫剂使用量（吨）.1',\n '脱硫设施运行时间（小时）.1', '脱硝还原剂消耗量（吨）.1', '燃料消耗量（吨）.1', '石灰石总量（吨）.1'],\n dtype='object')"
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"xswx_daily = pd.read_excel('./data/机器学习样表.xlsx', sheet_name=3)\n",
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"xswx_daily.columns"
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"metadata": {
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"collapsed": false,
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"cell_type": "code",
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"execution_count": 5,
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{
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"data": {
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"text/plain": " days 发电量_2（万千瓦时） 供热量_2（吉焦） 燃料消耗量_2（吨）\n149 2021-05-30 1033 0.0 4763.4\n150 2021-05-31 909 0.0 4516.5",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>days</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>149</th>\n <td>2021-05-30</td>\n <td>1033</td>\n <td>0.0</td>\n <td>4763.4</td>\n </tr>\n <tr>\n <th>150</th>\n <td>2021-05-31</td>\n <td>909</td>\n <td>0.0</td>\n <td>4516.5</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 5,
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"metadata": {},
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],
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"source": [
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"xswx_daily_unit_1 = xswx_daily[xswx_daily['日期'] >= '2021-01-01'][['日期', '发电量', '供热量(吉焦)', '燃料消耗量（吨）']].copy()\n",
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"xswx_daily_unit_1.columns = ['days', \"发电量_1（万千瓦时）\", '供热量_1（吉焦）', '燃料消耗量_1（吨）']\n",
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"xswx_daily_unit_2 = xswx_daily[xswx_daily['日期.1'] >= '2021-01-01'][\n",
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" ['日期.1', '发电量（万千瓦时）', '供热量（吉焦）', '燃料消耗量（吨）.1']].copy()\n",
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"xswx_daily_unit_2.columns = ['days', \"发电量_2（万千瓦时）\", '供热量_2（吉焦）', '燃料消耗量_2（吨）']\n",
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"xswx_daily_unit_2.tail(2)"
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{
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"data": {
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"text/plain": "DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',\n '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',\n '2021-01-09', '2021-01-10',\n ...\n '2021-05-22', '2021-05-23', '2021-05-24', '2021-05-25',\n '2021-05-26', '2021-05-27', '2021-05-28', '2021-05-29',\n '2021-05-30', '2021-05-31'],\n dtype='datetime64[ns]', length=151, freq='D')"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"min_start = min(xswx_daily_unit_1.days.min(), xswx_daily_unit_2.days.min())\n",
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"max_end = max(xswx_daily_unit_1.days.max(), xswx_daily_unit_2.days.max())\n",
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"date_range = pd.date_range(min_start, max_end, freq='D')\n",
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"date_range"
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],
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"metadata": {
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [],
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"source": [
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"xswx_daily_unit_1 = xswx_daily_unit_1.set_index('days').reindex(date_range).fillna(0)\n",
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"xswx_daily_unit_2 = xswx_daily_unit_2.set_index('days').reindex(date_range).fillna(0)"
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],
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"metadata": {
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"execution_count": 8,
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"outputs": [],
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"source": [
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"xswx_daily_data = pd.concat([xswx_daily_unit_1, xswx_daily_unit_2], axis=1)"
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],
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"metadata": {
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [],
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"source": [
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"xswx_daily_data['企业名称'] = '武乡西山发电有限责任公司'\n",
|
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"xswx_daily_data['发电量（万千瓦时）'] = xswx_daily_data['发电量_1（万千瓦时）'] + xswx_daily_data['发电量_2（万千瓦时）']\n",
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"xswx_daily_data['供热量（吉焦）'] = xswx_daily_data['供热量_1（吉焦）'] + xswx_daily_data['供热量_2（吉焦）']\n",
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"xswx_daily_data['燃料消耗量（吨）'] = xswx_daily_data['燃料消耗量_1（吨）'] + xswx_daily_data['燃料消耗量_2（吨）']"
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],
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"metadata": {
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"collapsed": false,
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [
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{
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"data": {
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"text/plain": " 发电量_1（万千瓦时） 供热量_1（吉焦） 燃料消耗量_1（吨） 发电量_2（万千瓦时） 供热量_2（吉焦） \\\n2021-01-01 952.0 11032.5 5478.8 893 0.0 \n2021-01-02 1127.0 11180.5 6125.4 1061 0.0 \n2021-01-03 1051.0 11197.7 5717.6 1053 0.0 \n2021-01-04 1179.0 11146.6 6172.5 1237 0.0 \n2021-01-05 1142.0 10922.4 6053.3 1082 0.0 \n... ... ... ... ... ... \n2021-05-27 0.0 0.0 0.0 1192 0.0 \n2021-05-28 0.0 0.0 0.0 1159 0.0 \n2021-05-29 0.0 0.0 0.0 998 0.0 \n2021-05-30 0.0 0.0 0.0 1033 0.0 \n2021-05-31 0.0 0.0 0.0 909 0.0 \n\n 燃料消耗量_2（吨） 企业名称 发电量（万千瓦时） 供热量（吉焦） 燃料消耗量（吨） \n2021-01-01 4689.7 武乡西山发电有限责任公司 1845.0 11032.5 10168.5 \n2021-01-02 5455.5 武乡西山发电有限责任公司 2188.0 11180.5 11580.9 \n2021-01-03 4060.5 武乡西山发电有限责任公司 2104.0 11197.7 9778.1 \n2021-01-04 5574.7 武乡西山发电有限责任公司 2416.0 11146.6 11747.2 \n2021-01-05 6363.9 武乡西山发电有限责任公司 2224.0 10922.4 12417.2 \n... ... ... ... ... ... \n2021-05-27 5684.3 武乡西山发电有限责任公司 1192.0 0.0 5684.3 \n2021-05-28 5349.3 武乡西山发电有限责任公司 1159.0 0.0 5349.3 \n2021-05-29 4851.2 武乡西山发电有限责任公司 998.0 0.0 4851.2 \n2021-05-30 4763.4 武乡西山发电有限责任公司 1033.0 0.0 4763.4 \n2021-05-31 4516.5 武乡西山发电有限责任公司 909.0 0.0 4516.5 \n\n[151 rows x 10 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>发电量_1（万千瓦时）</th>\n <th>供热量_1（吉焦）</th>\n <th>燃料消耗量_1（吨）</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n <th>企业名称</th>\n <th>发电量（万千瓦时）</th>\n <th>供热量（吉焦）</th>\n <th>燃料消耗量（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2021-01-01</th>\n <td>952.0</td>\n <td>11032.5</td>\n <td>5478.8</td>\n <td>893</td>\n <td>0.0</td>\n <td>4689.7</td>\n <td>武乡西山发电有限责任公司</td>\n <td>1845.0</td>\n <td>11032.5</td>\n <td>10168.5</td>\n </tr>\n <tr>\n <th>2021-01-02</th>\n <td>1127.0</td>\n <td>11180.5</td>\n <td>6125.4</td>\n <td>1061</td>\n <td>0.0</td>\n <td>5455.5</td>\n <td>武乡西山发电有限责任公司</td>\n <td>2188.0</td>\n <td>11180.5</td>\n <td>11580.9</td>\n </tr>\n <tr>\n <th>2021-01-03</th>\n <td>1051.0</td>\n <td>11197.7</td>\n <td>5717.6</td>\n <td>1053</td>\n <td>0.0</td>\n <td>4060.5</td>\n <td>武乡西山发电有限责任公司</td>\n <td>2104.0</td>\n <td>11197.7</td>\n <td>9778.1</td>\n </tr>\n <tr>\n <th>2021-01-04</th>\n <td>1179.0</td>\n <td>11146.6</td>\n <td>6172.5</td>\n <td>1237</td>\n <td>0.0</td>\n <td>5574.7</td>\n <td>武乡西山发电有限责任公司</td>\n <td>2416.0</td>\n <td>11146.6</td>\n <td>11747.2</td>\n </tr>\n <tr>\n <th>2021-01-05</th>\n <td>1142.0</td>\n <td>10922.4</td>\n <td>6053.3</td>\n <td>1082</td>\n <td>0.0</td>\n <td>6363.9</td>\n <td>武乡西山发电有限责任公司</td>\n <td>2224.0</td>\n <td>10922.4</td>\n <td>12417.2</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2021-05-27</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1192</td>\n <td>0.0</td>\n <td>5684.3</td>\n <td>武乡西山发电有限责任公司</td>\n <td>1192.0</td>\n <td>0.0</td>\n <td>5684.3</td>\n </tr>\n <tr>\n <th>2021-05-28</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1159</td>\n <td>0.0</td>\n <td>5349.3</td>\n <td>武乡西山发电有限责任公司</td>\n <td>1159.0</td>\n <td>0.0</td>\n <td>5349.3</td>\n </tr>\n <tr>\n <th>2021-05-29</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>998</td>\n <td>0.0</td>\n <td>4851.2</td>\n <td>武乡西山发电有限责任公司</td>\n <td>998.0</td>\n <td>0.0</td>\n <td>4851.2</td>\n </tr>\n <tr>\n <th>2021-05-30</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1033</td>\n <td>0.0</td>\n <td>4763.4</td>\n <td>武乡西山发电有限责任公司</td>\n <td>1033.0</td>\n <td>0.0</td>\n <td>4763.4</td>\n </tr>\n <tr>\n <th>2021-05-31</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>909</td>\n <td>0.0</td>\n <td>4516.5</td>\n <td>武乡西山发电有限责任公司</td>\n <td>909.0</td>\n <td>0.0</td>\n <td>4516.5</td>\n </tr>\n </tbody>\n</table>\n<p>151 rows × 10 columns</p>\n<
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"xswx_daily_data"
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"execution_count": 11,
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2022-10-25 15:11:12 +08:00
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"outputs": [
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"text/plain": " Unnamed: 0 Unnamed: 1 监控点名称 二氧化硫浓度(mg/m3) 氮氧化物浓度(mg/m3) \\\n0 2021.1.1 武乡西山发电有限责任公司 1号炉废气排放口 20.15 31.49 \n1 2021.1.2 武乡西山发电有限责任公司 1号炉废气排放口 20.27 31.65 \n2 2021.1.3 武乡西山发电有限责任公司 1号炉废气排放口 19.72 31.68 \n3 2021.1.4 武乡西山发电有限责任公司 1号炉废气排放口 17.64 31.51 \n4 2021.1.5 武乡西山发电有限责任公司 1号炉废气排放口 18.66 31.72 \n.. ... ... ... ... ... \n113 2021.4.26 武乡西山发电有限责任公司 1号炉废气排放口 0.22 0.01 \n114 2021.4.27 武乡西山发电有限责任公司 1号炉废气排放口 0.00 0.01 \n115 2021.4.28 武乡西山发电有限责任公司 1号炉废气排放口 0.01 0.00 \n116 2021.4.29 武乡西山发电有限责任公司 1号炉废气排放口 0.00 0.01 \n117 2021.4.30 武乡西山发电有限责任公司 1号炉废气排放口 0.00 0.02 \n\n 烟尘浓度(mg/m3) 流速(m/s) 流量(m3/h) Unnamed: 8 Unnamed: 9 ... \\\n0 2.49 11.24 1558502.96 NaN NaN ... \n1 2.51 12.03 1648629.50 NaN NaN ... \n2 2.50 12.11 1656682.63 NaN NaN ... \n3 2.52 11.79 1610496.46 NaN NaN ... \n4 2.50 11.97 1652330.08 NaN NaN ... \n.. ... ... ... ... ... ... \n113 0.19 0.00 0.00 NaN NaN ... \n114 0.21 0.00 0.00 NaN NaN ... \n115 0.25 0.00 0.00 NaN NaN ... \n116 0.26 0.00 0.00 NaN NaN ... \n117 0.56 0.00 0.00 NaN NaN ... \n\n Unnamed: 14 监控点名称.1 二氧化硫浓度(mg/m3).1 氮氧化物浓度(mg/m3).1 烟尘浓度(mg/m3).1 \\\n0 武乡西山发电有限责任公司 2号炉废气排放口 22.00 33.09 2.25 \n1 武乡西山发电有限责任公司 2号炉废气排放口 20.45 34.37 2.37 \n2 武乡西山发电有限责任公司 2号炉废气排放口 19.82 33.55 2.38 \n3 武乡西山发电有限责任公司 2号炉废气排放口 20.18 32.47 2.39 \n4 武乡西山发电有限责任公司 2号炉废气排放口 21.07 33.73 2.42 \n.. ... ... ... ... ... \n113 武乡西山发电有限责任公司 2号炉废气排放口 18.31 35.41 2.03 \n114 武乡西山发电有限责任公司 2号炉废气排放口 0.14 0.43 0.08 \n115 武乡西山发电有限责任公司 2号炉废气排放口 0.00 0.01 0.09 \n116 武乡西山发电有限责任公司 2号炉废气排放口 0.01 0.05 0.07 \n117 武乡西山发电有限责任公司 2号炉废气排放口 3.96 22.90 0.68 \n\n 流速(m/s).1 流量(m3/h).1 Unnamed: 21 Unnamed: 22 状态.1 \n0 5.00 731609.00 NaN NaN 正常运行 \n1 9.18 1326387.42 NaN NaN 正常运行 \n2 9.85 1417186.13 NaN NaN 正常运行 \n3 9.07 1304455.88 NaN NaN 正常运行 \n4 9.71 1390566.88 NaN NaN 正常运行 \n.. ... ... ... ... ... \n113 9.59 1316204.83 NaN NaN 正<><E6ADA3>
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},
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"execution_count": 11,
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2022-10-25 15:11:12 +08:00
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"xswx_emiss_data = pd.read_excel('data/机器学习样表_单位换算.xlsx', sheet_name=2)\n",
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"xswx_emiss_data"
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],
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2022-10-31 14:20:50 +08:00
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"execution_count": 12,
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2022-10-25 15:11:12 +08:00
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"outputs": [
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{
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"data": {
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"text/plain": " Unnamed: 0 二氧化硫浓度(mg/m3) 氮氧化物浓度(mg/m3) 烟尘浓度(mg/m3) 流速(m/s) \\\n0 2021.1.1 20.15 31.49 2.49 11.24 \n1 2021.1.2 20.27 31.65 2.51 12.03 \n2 2021.1.3 19.72 31.68 2.50 12.11 \n3 2021.1.4 17.64 31.51 2.52 11.79 \n4 2021.1.5 18.66 31.72 2.50 11.97 \n.. ... ... ... ... ... \n113 2021.4.26 0.22 0.01 0.19 0.00 \n114 2021.4.27 0.00 0.01 0.21 0.00 \n115 2021.4.28 0.01 0.00 0.25 0.00 \n116 2021.4.29 0.00 0.01 0.26 0.00 \n117 2021.4.30 0.00 0.02 0.56 0.00 \n\n 流量(m3/h) 状态 \n0 1558502.96 正常运行 \n1 1648629.50 正常运行 \n2 1656682.63 正常运行 \n3 1610496.46 正常运行 \n4 1652330.08 正常运行 \n.. ... ... \n113 0.00 停运 \n114 0.00 停运 \n115 0.00 停运 \n116 0.00 停运 \n117 0.00 停运 \n\n[118 rows x 7 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>二氧化硫浓度(mg/m3)</th>\n <th>氮氧化物浓度(mg/m3)</th>\n <th>烟尘浓度(mg/m3)</th>\n <th>流速(m/s)</th>\n <th>流量(m3/h)</th>\n <th>状态</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2021.1.1</td>\n <td>20.15</td>\n <td>31.49</td>\n <td>2.49</td>\n <td>11.24</td>\n <td>1558502.96</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2021.1.2</td>\n <td>20.27</td>\n <td>31.65</td>\n <td>2.51</td>\n <td>12.03</td>\n <td>1648629.50</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2021.1.3</td>\n <td>19.72</td>\n <td>31.68</td>\n <td>2.50</td>\n <td>12.11</td>\n <td>1656682.63</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2021.1.4</td>\n <td>17.64</td>\n <td>31.51</td>\n <td>2.52</td>\n <td>11.79</td>\n <td>1610496.46</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2021.1.5</td>\n <td>18.66</td>\n <td>31.72</td>\n <td>2.50</td>\n <td>11.97</td>\n <td>1652330.08</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>113</th>\n <td>2021.4.26</td>\n <td>0.22</td>\n <td>0.01</td>\n <td>0.19</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>114</th>\n <td>2021.4.27</td>\n <td>0.00</td>\n <td>0.01</td>\n <td>0.21</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>115</th>\n <td>2021.4.28</td>\n <td>0.01</td>\n <td>0.00</td>\n <td>0.25</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>116</th>\n <td>2021.4.29</td>\n <td>0.00</td>\n <td>0.01</td>\n <td>0.26</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>117</th>\n <td>2021.4.30</td>\n <td>0.00</td>\n <td>0.02</td>\n <td>0.56</td>\n <td>0.00</td>\n <td>0.00</td>\n <td>停运</td>\n </tr>\n </tbody>\n</table>\n<p>118 rows × 7 columns</p>\n</div>"
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xswx_emiss_data_1 = xswx_emiss_data[np.array(xswx_emiss_data.columns)[[0, 3, 4, 5, 6, 7, 10]]].copy()\n",
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"xswx_emiss_data_2 = xswx_emiss_data[np.array(xswx_emiss_data.columns)[[13, 16, 17, 18, 19, 20, 23]]].copy()\n",
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"xswx_emiss_data_1"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"outputs": [],
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"source": [
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"xswx_emiss_data_1.columns = ['days'] + xswx_emiss_data_1.columns[1:].tolist()\n",
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"xswx_emiss_data_2.columns = xswx_emiss_data_1.columns.tolist()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 14,
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"outputs": [],
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"source": [
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"xswx_emiss_data_1.index = pd.to_datetime(xswx_emiss_data_1.days)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"outputs": [],
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"source": [
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"xswx_emiss_data_2.index = pd.to_datetime(xswx_emiss_data_2.days)"
|
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],
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
|
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|
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"execution_count": 16,
|
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"outputs": [],
|
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"source": [
|
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"xswx_emiss_data_1.drop(columns=['days'], inplace=True)\n",
|
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|
|
|
"xswx_emiss_data_2.drop(columns=['days'], inplace=True)"
|
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"outputs": [],
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"source": [
|
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"xswx_emiss_data_1.columns = [f'机组1_{x}' for x in xswx_emiss_data_1.columns]\n",
|
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|
|
|
"xswx_emiss_data_2.columns = [f'机组2_{x}' for x in xswx_emiss_data_2.columns]"
|
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
|
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"cell_type": "code",
|
2022-10-31 14:20:50 +08:00
|
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"execution_count": 18,
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"outputs": [
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{
|
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"data": {
|
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|
"text/plain": "2021-01-01 1\n2021-03-17 1\n2021-03-29 1\n2021-03-28 1\n2021-03-27 1\n ..\n2021-02-05 1\n2021-02-04 1\n2021-02-03 1\n2021-02-02 1\n2021-04-30 1\nName: days, Length: 118, dtype: int64"
|
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|
|
},
|
2022-10-31 14:20:50 +08:00
|
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"execution_count": 18,
|
2022-10-25 15:11:12 +08:00
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"metadata": {},
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"output_type": "execute_result"
|
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}
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|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"xswx_emiss_data_1.reset_index().days.value_counts()"
|
|
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|
|
|
],
|
|
|
|
|
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"metadata": {
|
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"collapsed": false,
|
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
|
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"cell_type": "code",
|
2022-10-31 14:20:50 +08:00
|
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"execution_count": 64,
|
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"outputs": [],
|
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|
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"source": [
|
2022-10-31 14:20:50 +08:00
|
|
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|
"wxxs_save_data = pd.concat([xswx_daily_data, xswx_emiss_data_1, xswx_emiss_data_2], axis=1)\n",
|
|
|
|
|
|
"wxxs_save_data.index = wxxs_save_data.index.astype(str)"
|
2022-10-25 15:11:12 +08:00
|
|
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|
],
|
|
|
|
|
|
"metadata": {
|
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|
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|
"collapsed": false,
|
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"pycharm": {
|
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"name": "#%%\n"
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}
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}
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},
|
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{
|
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|
"cell_type": "markdown",
|
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|
|
"source": [
|
|
|
|
|
|
"### 邯郸东郊"
|
|
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|
|
|
],
|
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
|
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{
|
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|
"cell_type": "code",
|
2022-10-31 14:20:50 +08:00
|
|
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|
"execution_count": 20,
|
2022-10-25 15:11:12 +08:00
|
|
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"outputs": [
|
|
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|
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|
{
|
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"data": {
|
|
|
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|
|
"text/plain": "Index(['企业名称', '地址', '省份', '经度', '纬度', '机组数量', '单机容量（MW）', '生产设备类型',\n '锅炉额定蒸发量 t/h ', '汽轮机类型', '压力参数', '冷却方式', '脱硝工艺', '脱硫工艺', '除尘工艺', '日期',\n '机组编号', '投产日期', '燃料类型', '低位发热量（GJ/t）', '产灰量（吨）', '发电量（万千瓦时）', '供热量',\n '产渣量', '机组运行时间', '发电煤耗（克/千瓦时）', '燃料消耗量（吨）', 'Unnamed: 27', '日期.1',\n '机组编号.1', '投产日期.1', '燃料类型.1', '低位发热量（GJ/t）.1', '产灰量（吨）.1',\n '发电量（万千瓦时）.1', '供热量.1', '产渣量.1', '机组运行时间.1', '发电煤耗（克/千瓦时）.1',\n '燃料消耗量（吨）.1'],\n dtype='object')"
|
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|
|
},
|
2022-10-31 14:20:50 +08:00
|
|
|
|
"execution_count": 20,
|
2022-10-25 15:11:12 +08:00
|
|
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"metadata": {},
|
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|
"output_type": "execute_result"
|
|
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}
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|
],
|
|
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|
|
"source": [
|
|
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|
|
|
"hddj_daily = pd.read_excel('data/机器学习样表_单位换算.xlsx', sheet_name=5)\n",
|
|
|
|
|
|
"hddj_daily.columns"
|
|
|
|
|
|
],
|
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
|
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"cell_type": "code",
|
2022-10-31 14:20:50 +08:00
|
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|
"execution_count": 21,
|
2022-10-25 15:11:12 +08:00
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"outputs": [],
|
|
|
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|
|
"source": [
|
|
|
|
|
|
"hddj_daily_1 = hddj_daily[['日期', '发电量（万千瓦时）', '供热量', '燃料消耗量（吨）']].copy()\n",
|
|
|
|
|
|
"hddj_daily_1.columns = ['days', \"发电量_1（万千瓦时）\", '供热量_1（吉焦）', '燃料消耗量_1（吨）']\n",
|
|
|
|
|
|
"hddj_daily_2 = hddj_daily[['日期.1', '发电量（万千瓦时）.1', '供热量.1', '燃料消耗量（吨）.1']].copy()\n",
|
|
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|
|
|
"hddj_daily_2.columns = ['days', \"发电量_2（万千瓦时）\", '供热量_2（吉焦）', '燃料消耗量_2（吨）']"
|
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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2022-10-25 15:11:12 +08:00
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"outputs": [
|
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{
|
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"data": {
|
|
|
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|
|
"text/plain": "DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',\n '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',\n '2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',\n '2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',\n '2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20',\n '2022-01-21', '2022-01-22', '2022-01-23', '2022-01-24',\n '2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',\n '2022-01-29', '2022-01-30', '2022-01-31', '2022-02-01',\n '2022-02-02', '2022-02-03', '2022-02-04', '2022-02-05',\n '2022-02-06', '2022-02-07', '2022-02-08', '2022-02-09',\n '2022-02-10', '2022-02-11', '2022-02-12', '2022-02-13',\n '2022-02-14', '2022-02-15', '2022-02-16', '2022-02-17',\n '2022-02-18', '2022-02-19', '2022-02-20', '2022-02-21',\n '2022-02-22', '2022-02-23', '2022-02-24', '2022-02-25',\n '2022-02-26', '2022-02-27', '2022-02-28'],\n dtype='datetime64[ns]', freq='D')"
|
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|
|
},
|
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|
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"execution_count": 22,
|
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|
|
|
|
"metadata": {},
|
|
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|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"min_start = min(hddj_daily_1.days.min(), hddj_daily_2.days.min())\n",
|
|
|
|
|
|
"max_end = max(hddj_daily_1.days.max(), hddj_daily_2.days.max())\n",
|
|
|
|
|
|
"date_range = pd.date_range(min_start, max_end, freq='D')\n",
|
|
|
|
|
|
"date_range"
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|
],
|
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"metadata": {
|
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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|
},
|
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{
|
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|
"cell_type": "code",
|
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"execution_count": 23,
|
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"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"hddj_daily_1 = hddj_daily_1.set_index('days').reindex(date_range).fillna(0)\n",
|
|
|
|
|
|
"hddj_daily_2 = hddj_daily_2.set_index('days').reindex(date_range).fillna(0)"
|
|
|
|
|
|
],
|
|
|
|
|
|
"metadata": {
|
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|
|
|
"collapsed": false,
|
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
|
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{
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"cell_type": "code",
|
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|
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|
"execution_count": 24,
|
2022-10-25 15:11:12 +08:00
|
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"outputs": [
|
|
|
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{
|
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"data": {
|
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"text/plain": " 发电量_1（万千瓦时） 供热量_1（吉焦） 燃料消耗量_1（吨）\n2022-01-01 494.20 30829 2861\n2022-01-02 554.30 32122 2536\n2022-01-03 558.30 33451 2911\n2022-01-04 529.70 33179 3023\n2022-01-05 563.90 29731 3191\n2022-01-06 561.00 32505 3357\n2022-01-07 570.00 33189 3231\n2022-01-08 526.80 31881 2765\n2022-01-09 517.10 30799 2574\n2022-01-10 512.80 29277 2512\n2022-01-11 521.20 32460 2757\n2022-01-12 543.32 33593 3132\n2022-01-13 512.52 33326 2950\n2022-01-14 495.42 31417 2755\n2022-01-15 500.06 32434 2834\n2022-01-16 527.93 31986 3182\n2022-01-17 496.50 32268 3121\n2022-01-18 529.31 31814 3241\n2022-01-19 552.01 30414 3274\n2022-01-20 544.00 32416 3594\n2022-01-21 561.29 34300 3891\n2022-01-22 574.00 38342 4276\n2022-01-23 534.94 37444 3767\n2022-01-24 543.91 34539 3175\n2022-01-25 538.31 36753 3860\n2022-01-26 529.59 34148 3749\n2022-01-27 525.88 33630 3725\n2022-01-28 545.06 34181 3866\n2022-01-29 522.68 30637 3395\n2022-01-30 509.69 34254 3101\n2022-01-31 516.51 32145 2985\n2022-02-01 500.46 25687 2662\n2022-02-02 477.03 29061 2821\n2022-02-03 439.93 29598 2779\n2022-02-04 452.76 28709 2523\n2022-02-05 508.35 30133 2884\n2022-02-06 485.41 27635 2902\n2022-02-07 469.75 28259 2939\n2022-02-08 452.87 26685 2556\n2022-02-09 470.87 26037 2691\n2022-02-10 490.75 25530 2901\n2022-02-11 467.50 24359 2790\n2022-02-12 446.31 24180 2766\n2022-02-13 466.12 25274 3054\n2022-02-14 464.96 29338 2533\n2022-02-15 466.40 27394 2611\n2022-02-16 493.26 27639 2905\n2022-02-17 495.53 30228 2894\n2022-02-18 524.59 28126 3030\n2022-02-19 466.49 27575 2470\n2022-02-20 503.46 27424 2799\n2022-02-21 521.02 26965 3098\n2022-02-22 528.08 26886 3317\n2022-02-23 533.95 25022 3174\n2022-02-24 505.76 26727 3079\n2022-02-25 445.43 21930 2580\n2022-02-26 491.01 16645 2733\n2022-02-27 465.33 17884 2862\n2022-02-28 487.21 15457 3029",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>发电量_1（万千瓦时）</th>\n <th>供热量_1（吉焦）</th>\n <th>燃料消耗量_1（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2022-01-01</th>\n <td>494.20</td>\n <td>30829</td>\n <td>2861</td>\n </tr>\n <tr>\n <th>2022-01-02</th>\n <td>554.30</td>\n <td>32122</td>\n <td>2536</td>\n </tr>\n <tr>\n <th>2022-01-03</th>\n <td>558.30</td>\n <td>33451</td>\n <td>2911</td>\n </tr>\n <tr>\n <th>2022-01-04</th>\n <td>529.70</td>\n <td>33179</td>\n <td>3023</td>\n </tr>\n <tr>\n <th>2022-01-05</th>\n <td>563.90</td>\n <td>29731</td>\n <td>3191</td>\n </tr>\n <tr>\n <th>2022-01-06</th>\n <td>561.00</td>\n <td>32505</td>\n <td>3357</td>\n </tr>\n <tr>\n <th>2022-01-07</th>\n <td>570.00</td>\n <td>33189</td>\n <td>3231</td>\n </tr>\n <tr>\n <th>2022-01-08</th>\n <td>526.80</td>\n <td>31881</td>\n <td>2765</td>\n </tr>\n <tr>\n <th>2022-01-09</th>\n <td>517.10</td>\n <td>30799</td>\n <td>2574</td>\n </tr>\n <tr>\n <th>2022-01-10</th>\n <td>512.80</td>\n <td>29277</td>\n <td>2512</td>\n </tr>\n <tr>\n <th>2022-01-11</th>\n <td>521.20</td>\n <td>32460</td>\n <td>2757</td>\n </tr>\n <tr>\n <th>2022-01-12</th>\n <td>543.32</td>\n <td>33593</td>\n <td>3132</td>\n </tr>\n <tr>\n <th>2022-01-13</th>\n <td>512.52</td>\n <td>33326</td>\n <td>2950</td>\n </tr>\n <tr>\n <th>2022-01-14</th>\n <td>495.42</td>\n <td>31417</td>\n <td>2755</td>\n </tr>\n <tr>\n <th>2022-01-15</th>\n <td>500.06</td>\n <td>32434</td>\n <td>2834</td>\n </tr>\n <tr>\n <th>2022-01-16</th>\n <td>527.93</td>\n <td>31986</td>\n <td>3182</td>\n </tr>\n <tr>\n <th>2022-01-17</th>\n <td>496.50</td>\n <td>32268</td>\n <td>3121</td>\n </tr>\n <tr>\n <th>2022-01-18</th>\n <td>529.31</td>\n <td>31814</td>\n <td>3241</td>\n </tr>\n <tr>\n <th>2022-01-19</th>\n <td>552.01</td>\n <td>30414</td>\n <td>3274</td>\n </tr>\n <tr>\n <th>2022-01-20</th>\n <td>544.00</td>\n <td>32416</td>\n <td>3594</td>\n </tr>\n <tr>\n <th>2022-01-21</th>\n <td>561.29</td>\n <td>34300</td>\n <td>3891</td>\n </tr>\n <tr>\n <th>2022-01-22</th>\n <td>574.00</td>\n <td>38342</td>\n <td>4276</td>\n </tr>\n <tr>\n <th>2022-01-23</th>\n <td>534.94</td>\n <td>37444</td>\n <td>3767</td>\n </tr>\n <tr>\n <th>2022-01-24</th>\n <td>543.91</td>\n <td>34539</td>\n <td>3175</td>\n </tr>\n <tr>\n <th>2022-01-25</th>\n <td>538.31</td>\n <td>36753</td>\n <td>3860</td>\n </tr>\n <tr>\n <th>2022-01-26</th>\n <td>529.59</td>\n <td>34148</td>\n <td>3749</td>\n </tr>\n <tr>\n <th>2022-01-27</th>\n <td>525.88</td>\n <td>33630</td>\n <td>3725</td>\n </tr>\n <tr>\n <th>2022-01-28</th>\n <td>545.06</td>\n <td>34181</td>\n <td>3866</td>\n </tr>\n <tr>\n <th>2022-01-29</th>\n <td>522.68</td>\n <td>30637</td>\n <td>3395</td>\n </tr>\n <tr>\n <th>2022-01-30</th>\n <td>509.69</td>\n <td>34254</td>\n <td>3101</td>\n </tr>\n <tr>\n <th>2022-01-31</th>\n <td>516.51</td>\n <td>32145</td>\n <td>2985</td>\n </tr>\n <tr>\n <th>
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"hddj_daily_1"
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"outputs": [],
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"source": [
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"hddj_daily_data = pd.concat([hddj_daily_1, hddj_daily_2], axis=1)"
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"execution_count": 26,
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"data": {
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"text/plain": "Index(['时间', '企业名称', '监测点', '流量 （m3/h）', 'NOx浓度(mg/m3)', 'SO2浓度(mg/m3)',\n '烟尘浓度(mg/m3)', '含氧量（%）', '温度（℃）', '烟气湿度（%）', '烟气压力（千帕）', '烟气流速（m/s）',\n '状态', 'Unnamed: 13', '时间.1', '企业名称.1', '监测点.1', '流量 （m3/h）.1',\n 'NOx浓度(mg/m3).1', 'SO2浓度(mg/m3).1', '烟尘浓度(mg/m3).1', '含氧量（%）.1',\n '温度（℃）.1', '烟气湿度（%）.1', '烟气压力（千帕）.1', '烟气流速（m/s）.1', '状态.1'],\n dtype='object')"
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"execution_count": 26,
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],
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"source": [
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"hddj_emiss_data = pd.read_excel('data/机器学习样表_单位换算.xlsx', sheet_name=4)\n",
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"hddj_emiss_data.columns"
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"cell_type": "code",
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"execution_count": 27,
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"outputs": [
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{
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"data": {
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"text/plain": " 时间 企业名称 监测点 流量 （m3/h） \\\n0 2022-01-01 00:00:00 国电电力邯郸东郊热电有限责任公司 1号机组排口DA002（DCS） 930582.0 \n1 2022-01-01 01:00:00 国电电力邯郸东郊热电有限责任公司 1号机组排口DA002（DCS） 891316.8 \n\n NOx浓度(mg/m3) SO2浓度(mg/m3) 烟尘浓度(mg/m3) 含氧量（%） 温度（℃） 烟气湿度（%） ... \\\n0 13.379 13.158 3.438 6.435 48.604 14.695 ... \n1 19.312 17.951 2.798 7.164 47.819 14.156 ... \n\n 流量 （m3/h）.1 NOx浓度(mg/m3).1 SO2浓度(mg/m3).1 烟尘浓度(mg/m3).1 含氧量（%）.1 温度（℃）.1 \\\n0 1111474.8 18.171 18.171 0.476 7.065 48.307 \n1 1103194.8 20.524 22.773 0.479 7.130 48.887 \n\n 烟气湿度（%）.1 烟气压力（千帕）.1 烟气流速（m/s）.1 状态.1 \n0 13.253 -0.012 13.693 停运 \n1 13.734 -0.021 13.691 停运 \n\n[2 rows x 27 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>时间</th>\n <th>企业名称</th>\n <th>监测点</th>\n <th>流量 （m3/h）</th>\n <th>NOx浓度(mg/m3)</th>\n <th>SO2浓度(mg/m3)</th>\n <th>烟尘浓度(mg/m3)</th>\n <th>含氧量（%）</th>\n <th>温度（℃）</th>\n <th>烟气湿度（%）</th>\n <th>...</th>\n <th>流量 （m3/h）.1</th>\n <th>NOx浓度(mg/m3).1</th>\n <th>SO2浓度(mg/m3).1</th>\n <th>烟尘浓度(mg/m3).1</th>\n <th>含氧量（%）.1</th>\n <th>温度（℃）.1</th>\n <th>烟气湿度（%）.1</th>\n <th>烟气压力（千帕）.1</th>\n <th>烟气流速（m/s）.1</th>\n <th>状态.1</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-01-01 00:00:00</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>1号机组排口DA002（DCS）</td>\n <td>930582.0</td>\n <td>13.379</td>\n <td>13.158</td>\n <td>3.438</td>\n <td>6.435</td>\n <td>48.604</td>\n <td>14.695</td>\n <td>...</td>\n <td>1111474.8</td>\n <td>18.171</td>\n <td>18.171</td>\n <td>0.476</td>\n <td>7.065</td>\n <td>48.307</td>\n <td>13.253</td>\n <td>-0.012</td>\n <td>13.693</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-01-01 01:00:00</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>1号机组排口DA002（DCS）</td>\n <td>891316.8</td>\n <td>19.312</td>\n <td>17.951</td>\n <td>2.798</td>\n <td>7.164</td>\n <td>47.819</td>\n <td>14.156</td>\n <td>...</td>\n <td>1103194.8</td>\n <td>20.524</td>\n <td>22.773</td>\n <td>0.479</td>\n <td>7.130</td>\n <td>48.887</td>\n <td>13.734</td>\n <td>-0.021</td>\n <td>13.691</td>\n <td>停运</td>\n </tr>\n </tbody>\n</table>\n<p>2 rows × 27 columns</p>\n</div>"
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"source": [
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"hddj_emiss_data_1 = hddj_emiss_data[['时间', '流量 （m3/h）', 'NOx浓度(mg/m3)', 'SO2浓度(mg/m3)',\n",
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" '烟尘浓度(mg/m3)', '含氧量（%）', '温度（℃）', '烟气湿度（%）', '烟气压力（千帕）', '烟气流速（m/s）',\n",
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" '状态', ]].copy()\n",
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"hddj_emiss_data_2 = hddj_emiss_data[['时间.1', '流量 （m3/h）.1',\n",
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" 'NOx浓度(mg/m3).1', 'SO2浓度(mg/m3).1', '烟尘浓度(mg/m3).1', '含氧量（%）.1',\n",
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" '温度（℃）.1', '烟气湿度（%）.1', '烟气压力（千帕）.1', '烟气流速（m/s）.1', '状态.1']].copy()"
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"outputs": [],
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"source": [
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"hddj_emiss_data_1.columns = ['date'] + hddj_emiss_data_1.columns[1:].tolist()\n",
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"hddj_emiss_data_2.columns = hddj_emiss_data_1.columns.tolist()"
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"metadata": {
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"source": [
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"hddj_emiss_data_1['days'] = hddj_emiss_data_1.date.apply(lambda x: str(x).split(' ')[0])\n",
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"hddj_emiss_data_2['days'] = hddj_emiss_data_2.date.apply(lambda x: str(x).split(' ')[0])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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{
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"cell_type": "code",
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"execution_count": 31,
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"outputs": [],
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"source": [
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"num_cols = hddj_emiss_data_1.columns[1:-2]\n",
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"hddj_emiss_daily_1 = hddj_emiss_data_1.ffill().groupby('days')[num_cols].mean()\n",
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"hddj_emiss_daily_1['状态'] = '正常运行'\n",
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"hddj_emiss_daily_1.columns = [f\"机组1_{x}\" for x in hddj_emiss_daily_1.columns]\n",
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"hddj_emiss_daily_2 = hddj_emiss_data_2.ffill().groupby('days')[num_cols].mean()\n",
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"hddj_emiss_daily_2['状态'] = '正常运行'\n",
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"hddj_emiss_daily_2.columns = [f\"机组2_{x}\" for x in hddj_emiss_daily_2.columns]"
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"collapsed": false,
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{
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"cell_type": "code",
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"execution_count": 32,
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"outputs": [],
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"source": [
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"hddj_daily_data.index = hddj_daily_data.index.astype(str)\n",
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"hddj_daily_data['企业名称'] = \"国电电力邯郸东郊热电有限责任公司\""
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],
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{
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"cell_type": "code",
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"execution_count": 33,
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"outputs": [
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{
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"data": {
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"text/plain": " 发电量_1（万千瓦时） 供热量_1（吉焦） 燃料消耗量_1（吨） 发电量_2（万千瓦时） 供热量_2（吉焦） \\\n2022-01-01 494.20 30829 2861 536.50 324 \n2022-01-02 554.30 32122 2536 567.90 1008 \n2022-01-03 558.30 33451 2911 566.20 1296 \n2022-01-04 529.70 33179 3023 563.50 1248 \n2022-01-05 563.90 29731 3191 609.30 1362 \n2022-01-06 561.00 32505 3357 577.20 1236 \n2022-01-07 570.00 33189 3231 579.00 1035 \n2022-01-08 526.80 31881 2765 542.90 1323 \n2022-01-09 517.10 30799 2574 538.90 1305 \n2022-01-10 512.80 29277 2512 483.40 1335 \n2022-01-11 521.20 32460 2757 536.10 1299 \n2022-01-12 543.32 33593 3132 558.85 1368 \n2022-01-13 512.52 33326 2950 527.32 1398 \n2022-01-14 495.42 31417 2755 512.83 1536 \n2022-01-15 500.06 32434 2834 517.19 1242 \n2022-01-16 527.93 31986 3182 542.34 1116 \n2022-01-17 496.50 32268 3121 513.14 1362 \n2022-01-18 529.31 31814 3241 544.11 1305 \n2022-01-19 552.01 30414 3274 565.96 1161 \n2022-01-20 544.00 32416 3594 581.34 1329 \n2022-01-21 561.29 34300 3891 617.18 1383 \n2022-01-22 574.00 38342 4276 585.25 1368 \n2022-01-23 534.94 37444 3767 550.24 1242 \n2022-01-24 543.91 34539 3175 550.61 1362 \n2022-01-25 538.31 36753 3860 551.21 1131 \n2022-01-26 529.59 34148 3749 545.59 891 \n2022-01-27 525.88 33630 3725 537.33 777 \n2022-01-28 545.06 34181 3866 554.88 558 \n2022-01-29 522.68 30637 3395 544.47 399 \n2022-01-30 509.69 34254 3101 526.92 258 \n2022-01-31 516.51 32145 2985 528.13 210 \n2022-02-01 500.46 25687 2662 543.60 207 \n2022-02-02 477.03 29061 2821 534.40 186 \n2022-02-03 439.93 29598 2779 494.86 204 \n2022-02-04 452.76 28709 2523 493.99 282 \n2022-02-05 508.35 30133 2884 521.49 285 \n2022-02-06 485.41 27635 2902 503.13 363 \n2022-02-07 469.75 28259 2939 484.85 363 \n2022-02-08 452.87 26685 2556 481.92 603 \n2022-02-09 470.87 26037 2691 488.17 681 \n2022-02-10 490.75 25530 2901 508.89 591 \n2022-02-11 467.50 24359 2790 497.45 735 \n2022-02-12 446.31 24180 2766 474.04 621 \n2022-02-13 466.12 25274 3054 491.41 594 \n2022-02-14 464.96 29338 2533 491.96 651 \n2022-02-15 466.40 27394 2611 444.04 474 \n2022-02-16 493.26 27639 2905 420.10 1074 \n2022-02-17 495.53 30228 2894 469.32 1362 \n2022-02-18 524.59 28126 3030 531.28 1413 \n2022-02-19 466.49 27575 2470 497.11 1317 \n2022-02-20 503.46 27424 2799 460.91 1281 \n2022-02-21 521.02 26965 3098 482.89 1317 \n2022-02-22 5
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>发电量_1（万千瓦时）</th>\n <th>供热量_1（吉焦）</th>\n <th>燃料消耗量_1（吨）</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n <th>企业名称</th>\n <th>机组1_流量 （m3/h）</th>\n <th>机组1_NOx浓度(mg/m3)</th>\n <th>机组1_SO2浓度(mg/m3)</th>\n <th>...</th>\n <th>机组2_流量 （m3/h）</th>\n <th>机组2_NOx浓度(mg/m3)</th>\n <th>机组2_SO2浓度(mg/m3)</th>\n <th>机组2_烟尘浓度(mg/m3)</th>\n <th>机组2_含氧量（%）</th>\n <th>机组2_温度（℃）</th>\n <th>机组2_烟气湿度（%）</th>\n <th>机组2_烟气压力（千帕）</th>\n <th>机组2_烟气流速（m/s）</th>\n <th>机组2_状态</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2022-01-01</th>\n <td>494.20</td>\n <td>30829</td>\n <td>2861</td>\n <td>536.50</td>\n <td>324</td>\n <td>2752</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>941229.60</td>\n <td>18.200375</td>\n <td>18.165208</td>\n <td>...</td>\n <td>1095004.20</td>\n <td>19.098625</td>\n <td>16.495917</td>\n <td>0.477167</td>\n <td>7.384167</td>\n <td>49.570583</td>\n <td>14.332542</td>\n <td>-0.001208</td>\n <td>13.711958</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-01-02</th>\n <td>554.30</td>\n <td>32122</td>\n <td>2536</td>\n <td>567.90</td>\n <td>1008</td>\n <td>3086</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>992216.10</td>\n <td>19.536458</td>\n <td>18.721167</td>\n <td>...</td>\n <td>1113858.45</td>\n <td>20.497292</td>\n <td>15.455208</td>\n <td>0.453042</td>\n <td>6.918208</td>\n <td>48.557083</td>\n <td>13.511000</td>\n <td>-0.004167</td>\n <td>13.774167</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-01-03</th>\n <td>558.30</td>\n <td>33451</td>\n <td>2911</td>\n <td>566.20</td>\n <td>1296</td>\n <td>2914</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>1016053.05</td>\n <td>20.577917</td>\n <td>16.690083</td>\n <td>...</td>\n <td>1111516.35</td>\n <td>19.519958</td>\n <td>15.434792</td>\n <td>0.459625</td>\n <td>7.142917</td>\n <td>49.184958</td>\n <td>13.971208</td>\n <td>-0.003500</td>\n <td>13.844875</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-01-04</th>\n <td>529.70</td>\n <td>33179</td>\n <td>3023</td>\n <td>563.50</td>\n <td>1248</td>\n <td>2847</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>979135.35</td>\n <td>20.397917</td>\n <td>15.546958</td>\n <td>...</td>\n <td>1112429.55</td>\n <td>17.986042</td>\n <td>13.265125</td>\n <td>0.464458</td>\n <td>7.163542</td>\n <td>49.099500</td>\n <td>13.916375</td>\n <td>-0.008208</td>\n <td>13.842125</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-01-05</th>\n <td>563.90</td>\n <td>29731</td>\n <td>3191</td>\n <td>609.30</td>\n <td>1362</td>\n <td>3014</td>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>996159.15</td>\n <td>17.955417</td>\n <td>14.888333</td>\n <td>...</td>\n <td>1141153.80</td>\n <td>18.756125</td>\n <td>14.446000</td>\n <
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"hddj_save_data = pd.concat([hddj_daily_data, hddj_emiss_daily_1, hddj_emiss_daily_2], axis=1)\n",
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"hddj_save_data"
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 34,
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"outputs": [
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{
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"data": {
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"text/plain": "Index(['企业名称', 'address_reg', 'address1', '经度', '纬度', '机组数量', '单机容量（MW）',\n '生产设备类型', '锅炉额定蒸发量 t/h ', '汽轮机类型', '压力参数', '冷却方式', '脱硝工艺', '脱硫工艺',\n '除尘工艺', '燃料类型', '低位发热量（GJ/t）', 'Unnamed: 17', '日期', '机组编号', '投产日期',\n '产灰量（吨）', '产石膏量（吨）', '脱硝设施耗电量（千瓦时）', '运行时间（小时）', '除尘设施运行时间（小时）',\n '除尘耗电量（千瓦时）', '发电量（万千瓦时）', '供热量（万吉焦）', '产渣量', '脱硫剂使用量（吨）', '脱硫耗电量（千瓦时）',\n '脱硫设施运行时间（小时）', '脱硝还原剂消耗量（吨）', '脱硝运行时间（小时）', '燃料消耗量（吨）', 'Unnamed: 36',\n '日期.1', '机组编号.1', '投产日期.1', '产灰量（吨）.1', '产石膏量（吨）.1', '脱硝设施耗电量（千瓦时）.1',\n '运行时间（小时）.1', '除尘设施运行时间（小时）.1', '除尘耗电量（千瓦时）.1', '发电量（万千瓦时）.1',\n '供热量（万吉焦）.1', '产渣量.1', '脱硫剂使用量（吨）.1', '脱硫耗电量（千瓦时）.1', '脱硫设施运行时间（小时）.1',\n '脱硝还原剂消耗量（吨）.1', '脱硝运行时间（小时）.1', '燃料消耗量（吨）.1'],\n dtype='object')"
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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],
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"source": [
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"jtzh_daily = pd.read_excel('data/机器学习样表_单位换算.xlsx', sheet_name=7)\n",
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"jtzh_daily.columns"
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{
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"cell_type": "code",
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"execution_count": 35,
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2022-10-25 15:11:12 +08:00
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"outputs": [],
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"source": [
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2022-10-31 14:20:50 +08:00
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"jtzh_daily_1 = jtzh_daily[['日期', '发电量（万千瓦时）', '供热量（万吉焦）', '燃料消耗量（吨）']].copy()\n",
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"jtzh_daily_1.columns = ['days', \"发电量_1（万千瓦时）\", '供热量_1（吉焦）', '燃料消耗量_1（吨）']\n",
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"jtzh_daily_2 = jtzh_daily[['日期.1', '发电量（万千瓦时）.1', '供热量（万吉焦）.1', '燃料消耗量（吨）.1']].copy()\n",
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"jtzh_daily_2.columns = ['days', \"发电量_2（万千瓦时）\", '供热量_2（吉焦）', '燃料消耗量_2（吨）']"
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],
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"metadata": {
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"collapsed": false,
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{
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"cell_type": "code",
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2022-10-31 14:20:50 +08:00
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"execution_count": 36,
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2022-10-25 15:11:12 +08:00
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"outputs": [
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{
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"data": {
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2022-10-31 14:20:50 +08:00
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"text/plain": " days 发电量_2（万千瓦时） 供热量_2（吉焦） 燃料消耗量_2（吨）\n0 2022-05-01 0.000 0 0\n1 2022-05-02 0.000 0 0\n2 2022-05-03 0.000 0 0\n3 2022-05-04 0.000 0 0\n4 2022-05-05 0.000 0 0\n5 2022-05-06 0.000 0 0\n6 2022-05-07 0.000 0 426\n7 2022-05-08 218.196 0 1873\n8 2022-05-09 444.756 0 2350\n9 2022-05-10 552.114 0 2636\n10 2022-05-11 481.542 0 2432\n11 2022-05-12 439.788 0 2099\n12 2022-05-13 495.702 0 2340\n13 2022-05-14 486.624 0 2737\n14 2022-05-15 460.524 0 2461\n15 2022-05-16 505.578 0 2615\n16 2022-05-17 432.450 0 2593\n17 2022-05-18 509.586 0 2871\n18 2022-05-19 514.404 0 2637\n19 2022-05-20 514.434 0 2682\n20 2022-05-21 520.626 0 2581\n21 2022-05-22 500.040 0 2632\n22 2022-05-23 584.886 0 3187\n23 2022-05-24 514.488 0 2719\n24 2022-05-25 486.474 0 2599\n25 2022-05-26 501.366 0 2794\n26 2022-05-27 467.106 0 2401\n27 2022-05-28 504.900 0 2611\n28 2022-05-29 462.822 0 2846\n29 2022-05-30 528.960 0 2981\n30 2022-05-31 672.180 0 3560",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>days</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-05-01</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-05-02</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2022-05-03</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2022-05-04</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2022-05-05</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2022-05-06</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2022-05-07</td>\n <td>0.000</td>\n <td>0</td>\n <td>426</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2022-05-08</td>\n <td>218.196</td>\n <td>0</td>\n <td>1873</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2022-05-09</td>\n <td>444.756</td>\n <td>0</td>\n <td>2350</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2022-05-10</td>\n <td>552.114</td>\n <td>0</td>\n <td>2636</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2022-05-11</td>\n <td>481.542</td>\n <td>0</td>\n <td>2432</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2022-05-12</td>\n <td>439.788</td>\n <td>0</td>\n <td>2099</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2022-05-13</td>\n <td>495.702</td>\n <td>0</td>\n <td>2340</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2022-05-14</td>\n <td>486.624</td>\n <td>0</td>\n <td>2737</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2022-05-15</td>\n <td>460.524</td>\n <td>0</td>\n <td>2461</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2022-05-16</td>\n <td>505.578</td>\n <td>0</td>\n <td>2615</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2022-05-17</td>\n <td>432.450</td>\n <td>0</td>\n <td>2593</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2022-05-18</td>\n <td>509.586</td>\n <td>0</td>\n <td>2871</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2022-05-19</td>\n <td>514.404</td>\n <td>0</td>\n <td>2637</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2022-05-20</td>\n <td>514.434</td>\n <td>0</td>\n <td>2682</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2022-05-21</td>\n <td>520.626</td>\n <td>0</td>\n <td>2581</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2022-05-22</td>\n <td>500.040</td>\n <td>0</td>\n <td>2632</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2022-05-23</td>\n <td>584.886</td>\n <td>0</td>\n <td>3187</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2022-05-24</td>\n <td>514.488</td>\n <td>0</td>\n <td>2719</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2022-05-25</td>\n <td>486.474</td>\n <td>0</td>\n <td>2599</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2022-05-26</td>\n <td>501.366</td>\n <td>0</td>\n <td>2794</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2022-05-27</td>\n <td>467.106</td>\n <td>0</td>\n <td>2401</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2022-05-28</td>\n <td>
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{
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"data": {
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"text/plain": "DatetimeIndex(['2022-05-01', '2022-05-02', '2022-05-03', '2022-05-04',\n '2022-05-05', '2022-05-06', '2022-05-07', '2022-05-08',\n '2022-05-09', '2022-05-10', '2022-05-11', '2022-05-12',\n '2022-05-13', '2022-05-14', '2022-05-15', '2022-05-16',\n '2022-05-17', '2022-05-18', '2022-05-19', '2022-05-20',\n '2022-05-21', '2022-05-22', '2022-05-23', '2022-05-24',\n '2022-05-25', '2022-05-26', '2022-05-27', '2022-05-28',\n '2022-05-29', '2022-05-30', '2022-05-31'],\n dtype='datetime64[ns]', freq='D')"
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"execution_count": 37,
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"source": [
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"min_start = min(jtzh_daily_1.days.min(), jtzh_daily_2.days.min())\n",
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"max_end = max(jtzh_daily_1.days.max(), jtzh_daily_2.days.max())\n",
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"date_range = pd.date_range(min_start, max_end, freq='D')\n",
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"execution_count": 38,
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"outputs": [],
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"source": [
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"jtzh_daily_1 = jtzh_daily_1.set_index('days').reindex(date_range.astype(str)).fillna(0)\n",
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"jtzh_daily_2 = jtzh_daily_2.set_index('days').reindex(date_range.astype(str)).fillna(0)"
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{
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"data": {
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"text/plain": " 发电量_1（万千瓦时） 供热量_1（吉焦） 燃料消耗量_1（吨） 发电量_2（万千瓦时） 供热量_2（吉焦） \\\n2022-05-01 444.630 0 1889 0.000 0 \n2022-05-02 516.594 0 2622 0.000 0 \n2022-05-03 410.316 0 2233 0.000 0 \n2022-05-04 421.908 0 2203 0.000 0 \n2022-05-05 486.318 0 2524 0.000 0 \n2022-05-06 457.542 0 2343 0.000 0 \n2022-05-07 451.140 0 2278 0.000 0 \n2022-05-08 484.986 0 1120 218.196 0 \n2022-05-09 0.000 0 0 444.756 0 \n2022-05-10 0.000 0 0 552.114 0 \n2022-05-11 0.000 0 0 481.542 0 \n2022-05-12 0.000 0 0 439.788 0 \n2022-05-13 0.000 0 0 495.702 0 \n2022-05-14 0.000 0 121 486.624 0 \n2022-05-15 159.936 0 2251 460.524 0 \n2022-05-16 516.192 0 2543 505.578 0 \n2022-05-17 432.300 0 2432 432.450 0 \n2022-05-18 508.680 0 2810 509.586 0 \n2022-05-19 516.066 0 2972 514.404 0 \n2022-05-20 517.356 0 2623 514.434 0 \n2022-05-21 521.454 0 2759 520.626 0 \n2022-05-22 504.798 0 2545 500.040 0 \n2022-05-23 587.400 0 3294 584.886 0 \n2022-05-24 515.964 0 2633 514.488 0 \n2022-05-25 485.346 0 2694 486.474 0 \n2022-05-26 503.502 0 2619 501.366 0 \n2022-05-27 470.340 0 2510 467.106 0 \n2022-05-28 508.644 0 2753 504.900 0 \n2022-05-29 460.536 0 1163 462.822 0 \n2022-05-30 8.610 0 0 528.960 0 \n2022-05-31 0.000 0 0 672.180 0 \n\n 燃料消耗量_2（吨） \n2022-05-01 0 \n2022-05-02 0 \n2022-05-03 0 \n2022-05-04 0 \n2022-05-05 0 \n2022-05-06 0 \n2022-05-07 426 \n2022-05-08 1873 \n2022-05-09 2350 \n2022-05-10 2636 \n2022-05-11 2432 \n2022-05-12 2099 \n2022-05-13 2340 \n2022-05-14 2737 \n2022-05-15 2461 \n2022-05-16 2615 \n2022-05-17 2593 \n2022-05-18 2871 \n2022-05-19 2637 \n2022-05-20 2682 \n2022-05-21 2581 \n2022-05-22 2632 \n2022-05-23 3187 \n2022-05-24 2719 \n2022-05-25 2599 \n2022-05-26 2794 \n2022-05-27 2401 \n2022-05-28 2611 \n2022-05-29 2846 \n2022-05-30 2981 \n2022-05-31 3560 ",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>发电量_1（万千瓦时）</th>\n <th>供热量_1（吉焦）</th>\n <th>燃料消耗量_1（吨）</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2022-05-01</th>\n <td>444.630</td>\n <td>0</td>\n <td>1889</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-02</th>\n <td>516.594</td>\n <td>0</td>\n <td>2622</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-03</th>\n <td>410.316</td>\n <td>0</td>\n <td>2233</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-04</th>\n <td>421.908</td>\n <td>0</td>\n <td>2203</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-05</th>\n <td>486.318</td>\n <td>0</td>\n <td>2524</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-06</th>\n <td>457.542</td>\n <td>0</td>\n <td>2343</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2022-05-07</th>\n <td>451.140</td>\n <td>0</td>\n <td>2278</td>\n <td>0.000</td>\n <td>0</td>\n <td>426</td>\n </tr>\n <tr>\n <th>2022-05-08</th>\n <td>484.986</td>\n <td>0</td>\n <td>1120</td>\n <td>218.196</td>\n <td>0</td>\n <td>1873</td>\n </tr>\n <tr>\n <th>2022-05-09</th>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>444.756</td>\n <td>0</td>\n <td>2350</td>\n </tr>\n <tr>\n <th>2022-05-10</th>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>552.114</td>\n <td>0</td>\n <td>2636</td>\n </tr>\n <tr>\n <th>2022-05-11</th>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>481.542</td>\n <td>0</td>\n <td>2432</td>\n </tr>\n <tr>\n <th>2022-05-12</th>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>439.788</td>\n <td>0</td>\n <td>2099</td>\n </tr>\n <tr>\n <th>2022-05-13</th>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>495.702</td>\n <td>0</td>\n <td>2340</td>\n </tr>\n <tr>\n <th>2022-05-14</th>\n <td>0.000</td>\n <td>0</td>\n <td>121</td>\n <td>486.624</td>\n <td>0</td>\n <td>2737</td>\n </tr>\n <tr>\n <th>2022-05-15</th>\n <td>159.936</td>\n <td>0</td>\n <td>2251</td>\n <td>460.524</td>\n <td>0</td>\n <td>2461</td>\n </tr>\n <tr>\n <th>2022-05-16</th>\n <td>516.192</td>\n <td>0</td>\n <td>2543</td>\n <td>505.578</td>\n <td>0</td>\n <td>2615</td>\n </tr>\n <tr>\n <th>2022-05-17</th>\n <td>432.300</td>\n <td>0</td>\n <td>2432</td>\n <td>432.450</td>\n <td>0</td>\n <td>2593</td>\n </tr>\n <tr>\n <th>2022-05-18</th>\n <td>508.680</td>\n <td>0</td>\n <td>2810</td>\n <td>509.586</td>\n <td>0</td>\n <td>2871</td>\n </tr>\n <tr>\n <th>2022-05-19</th>\n <td>516.066</td>\n <td>0</td>\n <td>2972</td>\n <td>514.404</td>\n <td>0</td>\n <td>2637</td>\n </tr>\n <tr>\n <th>2022-05-20</th>\n <td>517.356</td>\n <td>0</td>\n <td>2623</td>\n <td>514.434</td>\n <td>0</td>\n <td>2682</td>\n </tr>\n <t
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"execution_count": 39,
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"source": [
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"jtzh_daily_data = pd.concat([jtzh_daily_1, jtzh_daily_2], axis=1)\n",
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"execution_count": 40,
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{
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"data": {
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"text/plain": "Index(['时间', '企业名称', '监测点', '流量 （m3/h）', 'NOx浓度(mg/m3)', 'SO2浓度(mg/m3)',\n '烟尘浓度(mg/m3)', '含氧量（%）', '烟气湿度（%）', '温度（℃）', '烟气流速（m/s）', '状态',\n 'Unnamed: 12', '时间.1', '企业名称.1', '监测点.1', '流量 （m3/h）.1',\n 'NOx浓度(mg/m3).1', 'SO2浓度(mg/m3).1', '烟尘浓度(mg/m3).1', '含氧量（%）.1',\n '烟气湿度（%）.1', '温度（℃）.1', '烟气流速（m/s）.1', '状态.1'],\n dtype='object')"
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"execution_count": 40,
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"source": [
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"jtzh_emiss_data = pd.read_excel('data/机器学习样表_单位换算.xlsx', sheet_name=6)\n",
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"jtzh_emiss_data.columns"
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"jtzh_emiss_data_1 = jtzh_emiss_data[['时间', '流量 （m3/h）', 'NOx浓度(mg/m3)', 'SO2浓度(mg/m3)',\n",
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" '烟尘浓度(mg/m3)', '含氧量（%）', '温度（℃）', '烟气湿度（%）', '烟气流速（m/s）',\n",
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" '状态', ]].copy()\n",
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"jtzh_emiss_data_2 = jtzh_emiss_data[['时间.1', '流量 （m3/h）.1',\n",
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" 'NOx浓度(mg/m3).1', 'SO2浓度(mg/m3).1', '烟尘浓度(mg/m3).1', '含氧量（%）.1',\n",
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" '温度（℃）.1', '烟气湿度（%）.1', '烟气流速（m/s）.1', '状态.1']].copy()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 42,
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"outputs": [],
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"source": [
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"jtzh_emiss_data_1.columns = ['date'] + jtzh_emiss_data_1.columns[1:].tolist()\n",
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"jtzh_emiss_data_2.columns = jtzh_emiss_data_1.columns.tolist()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 43,
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"outputs": [],
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"source": [
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"jtzh_emiss_data_1['days'] = jtzh_emiss_data_1.date.apply(lambda x: str(x).split(' ')[0])\n",
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"jtzh_emiss_data_2['days'] = jtzh_emiss_data_2.date.apply(lambda x: str(x).split(' ')[0])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 44,
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"outputs": [
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{
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"data": {
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"text/plain": " date 流量 （m3/h） NOx浓度(mg/m3) SO2浓度(mg/m3) 烟尘浓度(mg/m3) \\\n0 2022-05-01 00:00:00 705600 9.79 11.48 1.89 \n1 2022-05-01 01:00:00 716760 14.68 14.25 1.87 \n2 2022-05-01 02:00:00 685080 12.44 11.01 1.92 \n3 2022-05-01 03:00:00 687960 16.18 12.24 1.91 \n4 2022-05-01 04:00:00 691920 19.52 12.77 1.86 \n.. ... ... ... ... ... \n739 2022-05-31 19:00:00 56520 -0.13 0.06 0.68 \n740 2022-05-31 20:00:00 49680 -0.14 0.08 0.67 \n741 2022-05-31 21:00:00 47160 -0.15 0.07 0.68 \n742 2022-05-31 22:00:00 44280 -0.15 0.03 0.67 \n743 2022-05-31 23:00:00 38880 -0.15 0.01 0.66 \n\n 含氧量（%） 温度（℃） 烟气湿度（%） 烟气流速（m/s） 状态 days \n0 8.2 51.5 8.8 8.61 正常运行 2022-05-01 \n1 8.3 52.4 9.5 8.83 正常运行 2022-05-01 \n2 8.6 51.9 9.4 8.43 正常运行 2022-05-01 \n3 8.6 51.5 9.4 8.46 正常运行 2022-05-01 \n4 8.8 51.2 9.2 8.47 正常运行 2022-05-01 \n.. ... ... ... ... ... ... \n739 19.8 29.8 0.8 0.59 停运 2022-05-31 \n740 20.0 29.4 0.7 0.52 停运 2022-05-31 \n741 20.0 28.9 0.5 0.49 停运 2022-05-31 \n742 20.0 28.4 0.4 0.46 停运 2022-05-31 \n743 20.0 27.9 0.2 0.40 停运 2022-05-31 \n\n[744 rows x 11 columns]",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>date</th>\n <th>流量 （m3/h）</th>\n <th>NOx浓度(mg/m3)</th>\n <th>SO2浓度(mg/m3)</th>\n <th>烟尘浓度(mg/m3)</th>\n <th>含氧量（%）</th>\n <th>温度（℃）</th>\n <th>烟气湿度（%）</th>\n <th>烟气流速（m/s）</th>\n <th>状态</th>\n <th>days</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-05-01 00:00:00</td>\n <td>705600</td>\n <td>9.79</td>\n <td>11.48</td>\n <td>1.89</td>\n <td>8.2</td>\n <td>51.5</td>\n <td>8.8</td>\n <td>8.61</td>\n <td>正常运行</td>\n <td>2022-05-01</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-05-01 01:00:00</td>\n <td>716760</td>\n <td>14.68</td>\n <td>14.25</td>\n <td>1.87</td>\n <td>8.3</td>\n <td>52.4</td>\n <td>9.5</td>\n <td>8.83</td>\n <td>正常运行</td>\n <td>2022-05-01</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2022-05-01 02:00:00</td>\n <td>685080</td>\n <td>12.44</td>\n <td>11.01</td>\n <td>1.92</td>\n <td>8.6</td>\n <td>51.9</td>\n <td>9.4</td>\n <td>8.43</td>\n <td>正常运行</td>\n <td>2022-05-01</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2022-05-01 03:00:00</td>\n <td>687960</td>\n <td>16.18</td>\n <td>12.24</td>\n <td>1.91</td>\n <td>8.6</td>\n <td>51.5</td>\n <td>9.4</td>\n <td>8.46</td>\n <td>正常运行</td>\n <td>2022-05-01</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2022-05-01 04:00:00</td>\n <td>691920</td>\n <td>19.52</td>\n <td>12.77</td>\n <td>1.86</td>\n <td>8.8</td>\n <td>51.2</td>\n <td>9.2</td>\n <td>8.47</td>\n <td>正常运行</td>\n <td>2022-05-01</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>739</th>\n <td>2022-05-31 19:00:00</td>\n <td>56520</td>\n <td>-0.13</td>\n <td>0.06</td>\n <td>0.68</td>\n <td>19.8</td>\n <td>29.8</td>\n <td>0.8</td>\n <td>0.59</td>\n <td>停运</td>\n <td>2022-05-31</td>\n </tr>\n <tr>\n <th>740</th>\n <td>2022-05-31 20:00:00</td>\n <td>49680</td>\n <td>-0.14</td>\n <td>0.08</td>\n <td>0.67</td>\n <td>20.0</td>\n <td>29.4</td>\n <td>0.7</td>\n <td>0.52</td>\n <td>停运</td>\n <td>2022-05-31</td>\n </tr>\n <tr>\n <th>741</th>\n <td>2022-05-31 21:00:00</td>\n <td>47160</td>\n <td>-0.15</td>\n <td>0.07</td>\n <td>0.68</td>\n <td>20.0</td>\n <td>28.9</td>\n <td>0.5</td>\n <td>0.49</td>\n <td>停运</td>\n <td>2022-05-31</td>\n </tr>\n <tr>\n <th>742</th>\n <td>2022-05-31 22:00:00</td>\n <td>44280</td>\n <td>-0.15</td>\n <td>0.03</td>\n <td>0.67</td>\n <td>20.0</td>\n <td>28.4</td>\n <td>0.4</td>\n <td>0.46</td>\n <td>停运</td>\n <td>2022-05-31</td>\n </tr>\n <tr>\n <th>743</th>\n <td>2022-05-31 23:00:00</td>\n <td>38880</td>\n <td>-0.15</td>\n <td>0.01</td>\n <td>0.66</td>\n <td>20.0</td>\n <td>27.9</td>\n <td>0.2</td>\n <td>0.40</td>\n <td>停运</td>\n <td>2022-05-31</td>\n </tr>\n </tbody>\n</table>\n<p>744 rows × 11 columns</p>\n</div>"
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},
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"jtzh_emiss_data_1"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"outputs": [],
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"source": [
|
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"num_cols = jtzh_emiss_data_1.columns[1:-2]\n",
|
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"jtzh_emiss_daily_1 = jtzh_emiss_data_1.ffill().groupby('days')[num_cols].mean()\n",
|
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"jtzh_emiss_daily_1['状态'] = jtzh_emiss_data_1.ffill().groupby('days')['状态'].apply(lambda x: x.value_counts().index[0])\n",
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"jtzh_emiss_daily_1.columns = [f\"机组1_{x}\" for x in jtzh_emiss_daily_1.columns]\n",
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"\n",
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"jtzh_emiss_daily_2 = jtzh_emiss_data_2.ffill().groupby('days')[num_cols].mean()\n",
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"jtzh_emiss_daily_2['状态'] = jtzh_emiss_data_1.ffill().groupby('days')['状态'].apply(lambda x: x.value_counts().index[0])\n",
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"jtzh_emiss_daily_2.columns = [f\"机组2_{x}\" for x in jtzh_emiss_daily_2.columns]"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"outputs": [
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{
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"data": {
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"text/plain": " 机组1_流量 （m3/h） 机组1_NOx浓度(mg/m3) 机组1_SO2浓度(mg/m3) \\\ndays \n2022-05-01 820965.0 12.952083 12.856250 \n2022-05-02 907380.0 14.641667 11.846250 \n2022-05-03 771360.0 14.414167 12.434167 \n2022-05-04 780390.0 14.922500 12.910417 \n2022-05-05 870075.0 14.102917 13.716667 \n2022-05-06 816300.0 14.321667 14.458750 \n2022-05-07 804405.0 16.310833 13.279583 \n2022-05-08 845610.0 18.585417 13.300417 \n2022-05-09 26370.0 6.152083 0.403750 \n2022-05-10 269970.0 -0.208333 0.264583 \n2022-05-11 163470.0 -0.147917 0.288333 \n2022-05-12 27720.0 -0.182083 0.225417 \n2022-05-13 50760.0 -0.140833 0.102917 \n2022-05-14 48540.0 -0.112500 0.052083 \n2022-05-15 602865.0 85.984583 6.295833 \n2022-05-16 912705.0 14.561250 13.784583 \n2022-05-17 816165.0 13.918333 12.802083 \n2022-05-18 892620.0 14.547083 13.451250 \n2022-05-19 904860.0 15.437083 13.886250 \n2022-05-20 906690.0 13.552917 13.295000 \n2022-05-21 931575.0 16.459583 11.678333 \n2022-05-22 891885.0 15.156250 13.014167 \n2022-05-23 1000560.0 14.451667 13.710833 \n2022-05-24 921300.0 13.775000 12.157083 \n2022-05-25 867765.0 15.344167 12.987083 \n2022-05-26 902100.0 14.439167 13.062917 \n2022-05-27 865320.0 14.760000 12.725417 \n2022-05-28 925965.0 15.012917 14.615833 \n2022-05-29 861255.0 14.035000 12.818750 \n2022-05-30 71640.0 0.266667 0.770417 \n2022-05-31 53550.0 -0.043333 0.085417 \n\n 机组1_烟尘浓度(mg/m3) 机组1_含氧量（%） 机组1_温度（℃） 机组1_烟气湿度（%） \\\ndays \n2022-05-01 1.787500 7.795833 52.320833 9.204167 \n2022-05-02 1.717917 7.295833 53.145833 9.016667 \n2022-05-03 1.850833 7.650000 51.841667 8.750000 \n2022-05-04 1.922083 7.462500 51.808333 8.495833 \n2022-05-05 1.981667 7.195833 52.354167 8.725000 \n2022-05-06 1.729583 7.433333 51.462500 8.858333 \n2022-05-07 1.837917 7.333333 51.025000 8.595833 \n2022-05-08 1.906667 7.862500 51.854167 9.483333 \n2022-05-09 0.651250 19.887500 34.925000 2.458333 \n2022-05-10 1.107500 19.912500 32.612500 0.000000 \n2022-05-11 1.262083 20.100000 22.850000 0.000000 \n2022-05-12 0.636667 20.216667 19.625000 0.000000 \n2022-05-13 0.629583 20.270833 18.637500 0.000000 \n2022-05-14 0.626667 20.237500 21.245833 0.345833 \n2022-05-15 1.590833 13.391667 39.200000 4.379167 \n2022-05-16 1.407500 7.279167 53.104167 9.058333 \n2022-05-17 1.493333 7.700000 53.766667 9.658333 \n2022-05-18 1.721667 7.141667 52.816667 8.712500 \n2022-05-19 1.705833 7.012500 52.487500 7.966667 \n2022-05-20 1.815417 7.041667 53.208333 8.454167 \n2022-05-21 1.968750 7.241667 53.270833 7.487500 \n2022-05-22 1.800000 7.120833 52.929167 8.608333 \n2022-05-23 2.156667 6.716667 53.529167 8.504167 \n2022-05-24 2.155833 7.191667 53.195833 8.562500 \n2022-05-25 1.666250 7.337500
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>机组1_流量 （m3/h）</th>\n <th>机组1_NOx浓度(mg/m3)</th>\n <th>机组1_SO2浓度(mg/m3)</th>\n <th>机组1_烟尘浓度(mg/m3)</th>\n <th>机组1_含氧量（%）</th>\n <th>机组1_温度（℃）</th>\n <th>机组1_烟气湿度（%）</th>\n <th>机组1_烟气流速（m/s）</th>\n <th>机组1_状态</th>\n </tr>\n <tr>\n <th>days</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2022-05-01</th>\n <td>820965.0</td>\n <td>12.952083</td>\n <td>12.856250</td>\n <td>1.787500</td>\n <td>7.795833</td>\n <td>52.320833</td>\n <td>9.204167</td>\n <td>10.088750</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-02</th>\n <td>907380.0</td>\n <td>14.641667</td>\n <td>11.846250</td>\n <td>1.717917</td>\n <td>7.295833</td>\n <td>53.145833</td>\n <td>9.016667</td>\n <td>11.147917</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-03</th>\n <td>771360.0</td>\n <td>14.414167</td>\n <td>12.434167</td>\n <td>1.850833</td>\n <td>7.650000</td>\n <td>51.841667</td>\n <td>8.750000</td>\n <td>9.415417</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-04</th>\n <td>780390.0</td>\n <td>14.922500</td>\n <td>12.910417</td>\n <td>1.922083</td>\n <td>7.462500</td>\n <td>51.808333</td>\n <td>8.495833</td>\n <td>9.496250</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-05</th>\n <td>870075.0</td>\n <td>14.102917</td>\n <td>13.716667</td>\n <td>1.981667</td>\n <td>7.195833</td>\n <td>52.354167</td>\n <td>8.725000</td>\n <td>10.636667</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-06</th>\n <td>816300.0</td>\n <td>14.321667</td>\n <td>14.458750</td>\n <td>1.729583</td>\n <td>7.433333</td>\n <td>51.462500</td>\n <td>8.858333</td>\n <td>9.971667</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-07</th>\n <td>804405.0</td>\n <td>16.310833</td>\n <td>13.279583</td>\n <td>1.837917</td>\n <td>7.333333</td>\n <td>51.025000</td>\n <td>8.595833</td>\n <td>9.781667</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-08</th>\n <td>845610.0</td>\n <td>18.585417</td>\n <td>13.300417</td>\n <td>1.906667</td>\n <td>7.862500</td>\n <td>51.854167</td>\n <td>9.483333</td>\n <td>10.417500</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-09</th>\n <td>26370.0</td>\n <td>6.152083</td>\n <td>0.403750</td>\n <td>0.651250</td>\n <td>19.887500</td>\n <td>34.925000</td>\n <td>2.458333</td>\n <td>0.285000</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>2022-05-10</th>\n <td>269970.0</td>\n <td>-0.208333</td>\n <td>0.264583</td>\n <td>1.107500</td>\n <td>19.912500</td>\n <td>32.612500</td>\n <td>0.000000</td>\n <td>2.819583</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>2022-05-11</th>\n <td>163470.0</td>\n <td>-0.147917</td>\n <td>0.288333</td>\n <td>1.262083</td>\n <td>20.100000</td>\n <td>22.850000</td>\n <td>0.000000</td>\n <td>1.665833</td>\n <td>停运</td>\n </tr>\n <tr>\n <th>2022-05-12</th>\n <td>27720.0
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"execution_count": 50,
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"source": [
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"jtzh_emiss_daily_1"
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],
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"metadata": {
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"cell_type": "code",
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"execution_count": 47,
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"outputs": [],
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"source": [
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"jtzh_daily_data['企业名称'] = \"建投遵化热电有限责任公司\"\n",
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"jtzh_save_data = pd.concat([jtzh_daily_data, jtzh_emiss_daily_1, jtzh_emiss_daily_2], axis=1)"
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],
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"cell_type": "code",
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"execution_count": 48,
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"outputs": [
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{
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"data": {
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"text/plain": " 发电量_1（万千瓦时） 供热量_1（吉焦） 燃料消耗量_1（吨） 发电量_2（万千瓦时） 供热量_2（吉焦） \\\n2022-05-01 444.630 0 1889 0.000 0 \n2022-05-02 516.594 0 2622 0.000 0 \n2022-05-03 410.316 0 2233 0.000 0 \n2022-05-04 421.908 0 2203 0.000 0 \n2022-05-05 486.318 0 2524 0.000 0 \n2022-05-06 457.542 0 2343 0.000 0 \n2022-05-07 451.140 0 2278 0.000 0 \n2022-05-08 484.986 0 1120 218.196 0 \n2022-05-09 0.000 0 0 444.756 0 \n2022-05-10 0.000 0 0 552.114 0 \n2022-05-11 0.000 0 0 481.542 0 \n2022-05-12 0.000 0 0 439.788 0 \n2022-05-13 0.000 0 0 495.702 0 \n2022-05-14 0.000 0 121 486.624 0 \n2022-05-15 159.936 0 2251 460.524 0 \n2022-05-16 516.192 0 2543 505.578 0 \n2022-05-17 432.300 0 2432 432.450 0 \n2022-05-18 508.680 0 2810 509.586 0 \n2022-05-19 516.066 0 2972 514.404 0 \n2022-05-20 517.356 0 2623 514.434 0 \n2022-05-21 521.454 0 2759 520.626 0 \n2022-05-22 504.798 0 2545 500.040 0 \n2022-05-23 587.400 0 3294 584.886 0 \n2022-05-24 515.964 0 2633 514.488 0 \n2022-05-25 485.346 0 2694 486.474 0 \n2022-05-26 503.502 0 2619 501.366 0 \n2022-05-27 470.340 0 2510 467.106 0 \n2022-05-28 508.644 0 2753 504.900 0 \n2022-05-29 460.536 0 1163 462.822 0 \n2022-05-30 8.610 0 0 528.960 0 \n2022-05-31 0.000 0 0 672.180 0 \n\n 燃料消耗量_2（吨） 企业名称 机组1_流量 （m3/h） 机组1_NOx浓度(mg/m3) \\\n2022-05-01 0 建投遵化热电有限责任公司 820965.0 12.952083 \n2022-05-02 0 建投遵化热电有限责任公司 907380.0 14.641667 \n2022-05-03 0 建投遵化热电有限责任公司 771360.0 14.414167 \n2022-05-04 0 建投遵化热电有限责任公司 780390.0 14.922500 \n2022-05-05 0 建投遵化热电有限责任公司 870075.0 14.102917 \n2022-05-06 0 建投遵化热电有限责任公司 816300.0 14.321667 \n2022-05-07 426 建投遵化热电有限责任公司 804405.0 16.310833 \n2022-05-08 1873 建投遵化热电有限责任公司 845610.0 18.585417 \n2022-05-09 2350 建投遵化热电有限责任公司 26370.0 6.152083 \n2022-05-10 2636 建投遵化热电有限责任公司 269970.0 -0.208333 \n2022-05-11 2432 建投遵化热电有限责任公司 163470.0 -0.147917 \n2022-05-12 2099 建投遵化热电有限责任公司 27720.0 -0.182083 \n2022-05-13 2340 建投遵化热电有限责任公司 50760.0 -0.140833 \n2022-05-14 2737 建投遵化热电有限责任公司 48540.0 -0.112500 \n2022-05-15 2461 建投遵化热电有限责任公司 602865.0 85.984583 \n20
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>发电量_1（万千瓦时）</th>\n <th>供热量_1（吉焦）</th>\n <th>燃料消耗量_1（吨）</th>\n <th>发电量_2（万千瓦时）</th>\n <th>供热量_2（吉焦）</th>\n <th>燃料消耗量_2（吨）</th>\n <th>企业名称</th>\n <th>机组1_流量 （m3/h）</th>\n <th>机组1_NOx浓度(mg/m3)</th>\n <th>机组1_SO2浓度(mg/m3)</th>\n <th>...</th>\n <th>机组1_状态</th>\n <th>机组2_流量 （m3/h）</th>\n <th>机组2_NOx浓度(mg/m3)</th>\n <th>机组2_SO2浓度(mg/m3)</th>\n <th>机组2_烟尘浓度(mg/m3)</th>\n <th>机组2_含氧量（%）</th>\n <th>机组2_温度（℃）</th>\n <th>机组2_烟气湿度（%）</th>\n <th>机组2_烟气流速（m/s）</th>\n <th>机组2_状态</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2022-05-01</th>\n <td>444.630</td>\n <td>0</td>\n <td>1889</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>建投遵化热电有限责任公司</td>\n <td>820965.0</td>\n <td>12.952083</td>\n <td>12.856250</td>\n <td>...</td>\n <td>正常运行</td>\n <td>64290.0</td>\n <td>0.169583</td>\n <td>-1.430417</td>\n <td>0.210833</td>\n <td>20.512500</td>\n <td>13.150000</td>\n <td>0.000000</td>\n <td>0.630417</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-02</th>\n <td>516.594</td>\n <td>0</td>\n <td>2622</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>建投遵化热电有限责任公司</td>\n <td>907380.0</td>\n <td>14.641667</td>\n <td>11.846250</td>\n <td>...</td>\n <td>正常运行</td>\n <td>71235.0</td>\n <td>0.143333</td>\n <td>-1.401250</td>\n <td>0.211667</td>\n <td>20.416667</td>\n <td>15.525000</td>\n <td>0.000000</td>\n <td>0.704583</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-03</th>\n <td>410.316</td>\n <td>0</td>\n <td>2233</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>建投遵化热电有限责任公司</td>\n <td>771360.0</td>\n <td>14.414167</td>\n <td>12.434167</td>\n <td>...</td>\n <td>正常运行</td>\n <td>71370.0</td>\n <td>1.227500</td>\n <td>-1.264167</td>\n <td>0.218333</td>\n <td>19.479167</td>\n <td>22.129167</td>\n <td>0.200000</td>\n <td>0.722917</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-04</th>\n <td>421.908</td>\n <td>0</td>\n <td>2203</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>建投遵化热电有限责任公司</td>\n <td>780390.0</td>\n <td>14.922500</td>\n <td>12.910417</td>\n <td>...</td>\n <td>正常运行</td>\n <td>73785.0</td>\n <td>0.134583</td>\n <td>-1.037500</td>\n <td>0.214167</td>\n <td>20.095833</td>\n <td>26.258333</td>\n <td>0.016667</td>\n <td>0.757500</td>\n <td>正常运行</td>\n </tr>\n <tr>\n <th>2022-05-05</th>\n <td>486.318</td>\n <td>0</td>\n <td>2524</td>\n <td>0.000</td>\n <td>0</td>\n <td>0</td>\n <td>建投遵化热电有限责任公司</td>\n <td>870075.0</td>\n <td>14.102917</td>\n <td>13.716667</td>\n <td>...</td>\n <td>正常运行</td>\n <td>73605.0</td>\n <td>0.075417</td>\n <td>-0.920417</td>\n <td>0.212083</td>\n <td>20.150000</td>\n <td>23.062500</td>\n <td>0.212500</td>\n <td>0.750000</t
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"execution_count": 48,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"jtzh_save_data"
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],
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"metadata": {
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{
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"cell_type": "code",
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"execution_count": 65,
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"outputs": [],
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"source": [
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"writer = pd.ExcelWriter('data/电厂日数据.xlsx',engine='openpyxl')\n",
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"\n",
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"zjxz_save_data.to_excel(writer,\"浙江秀舟\")\n",
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"wxxs_save_data.to_excel(writer,\"武乡西山\")\n",
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"hddj_save_data.to_excel(writer,\"邯郸东郊\")\n",
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"jtzh_save_data.to_excel(writer,\"建投遵化\")\n",
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"\n",
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"writer.save()\n",
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"writer.close()"
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],
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"metadata": {
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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}
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2022-10-25 15:11:12 +08:00
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}
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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"file_extension": ".py",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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