813 lines
185 KiB
Plaintext
813 lines
185 KiB
Plaintext
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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"
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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": 2,
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"outputs": [
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{
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"data": {
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"text/plain": "dict_keys(['浙江秀舟', '武乡西山', '邯郸东郊', '建投遵化'])"
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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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"daily_data = pd.read_excel('data/电厂日数据.xlsx', sheet_name=None)\n",
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"daily_data.keys()"
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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": 3,
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"outputs": [],
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"source": [
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"zjxz_daily = daily_data.get('浙江秀舟')\n",
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"wxxs_daily = daily_data.get('武乡西山')\n",
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"hddj_daily = daily_data.get('邯郸东郊')\n",
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"jtzh_daily = daily_data.get('建投遵化')"
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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": 4,
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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） 流量 （m3/h） 氮氧化物浓度(mg/m3) \\\n0 2018-10-01 156796.0 6536.83 323 162345.192917 24.515087 \n1 2018-10-02 133984.0 2484.64 218 140175.330833 18.829456 \n2 2018-10-03 134023.0 3020.83 212 154686.184167 20.891797 \n3 2018-10-04 124765.0 5599.23 223 120345.545833 18.641687 \n4 2018-10-05 134414.0 4702.65 243 162533.103542 22.031219 \n\n 含氧量（%） 温度（℃） 二氧化硫浓度(mg/m3) 烟尘浓度(mg/m3) 发电量（万千瓦时） \n0 9.900000 51.250000 4.721475 0.176932 15.6796 \n1 9.400000 50.679167 3.698115 0.161356 13.3984 \n2 8.550000 52.808333 6.381178 0.117507 13.4023 \n3 10.202083 48.854167 2.393757 0.761287 12.4765 \n4 11.497917 45.783333 0.338422 1.842860 13.4414 ",
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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>流量 （m3/h）</th>\n <th>氮氧化物浓度(mg/m3)</th>\n <th>含氧量（%）</th>\n <th>温度（℃）</th>\n <th>二氧化硫浓度(mg/m3)</th>\n <th>烟尘浓度(mg/m3)</th>\n <th>发电量（万千瓦时）</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2018-10-01</td>\n <td>156796.0</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 <td>15.6796</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2018-10-02</td>\n <td>133984.0</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 <td>13.3984</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2018-10-03</td>\n <td>134023.0</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 <td>13.4023</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2018-10-04</td>\n <td>124765.0</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 <td>12.4765</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2018-10-05</td>\n <td>134414.0</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 <td>13.4414</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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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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],
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"source": [
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"zjxz_daily['发电量（万千瓦时）'] = zjxz_daily['发电量（千瓦时）'] / 10000\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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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [],
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"source": [
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"zjxz_daily.rename(columns={'流量 （m3/h）': 'flow', '温度（℃）': 'temperature', '含氧量（%）': 'r_O2'}, inplace=True)\n",
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"zjxz_daily['c_smoke'] = zjxz_daily['flow'] * zjxz_daily['烟尘浓度(mg/m3)']\n",
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"zjxz_daily['c_NO2'] = zjxz_daily['flow'] * zjxz_daily['氮氧化物浓度(mg/m3)']\n",
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"zjxz_daily['c_SO2'] = zjxz_daily['flow'] * zjxz_daily['二氧化硫浓度(mg/m3)']\n",
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"zjxz_daily['num_workers'] = 2\n",
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"zjxz_daily['企业名称'] = '浙江秀舟热电有限公司'"
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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": 6,
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"outputs": [],
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"source": [
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"zjxz_daily_final = zjxz_daily[\n",
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" ['days', '企业名称', 'r_O2', 'temperature', '发电量（万千瓦时）', '供热量（吉焦）', 'c_smoke', 'c_NO2', 'c_SO2', 'flow', 'num_workers', '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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"execution_count": 7,
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"outputs": [],
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"source": [
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"wxxs_daily.rename(columns={'Unnamed: 0': '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": 8,
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"outputs": [],
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"source": [
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"num_cols = [x for x in wxxs_daily.columns if x not in ['days', '企业名称', '机组1_状态', '机组2_状态']]"
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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": 9,
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"outputs": [
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{
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"data": {
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"text/plain": "(112, 23)"
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},
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"execution_count": 9,
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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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"wxxs_daily.dropna(inplace=True)\n",
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"wxxs_daily = wxxs_daily[~((wxxs_daily['机组1_状态'] == '停运') & (wxxs_daily['机组2_状态'] == '停运'))].copy()\n",
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"wxxs_daily.shape"
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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": 10,
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"outputs": [
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{
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"data": {
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"text/plain": "Index(['days', '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）',\n '供热量_2（吉焦）', '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）',\n '机组1_二氧化硫浓度(mg/m3)', '机组1_氮氧化物浓度(mg/m3)', '机组1_烟尘浓度(mg/m3)',\n '机组1_流速(m/s)', '机组1_流量(m3/h)', '机组1_状态', '机组2_二氧化硫浓度(mg/m3)',\n '机组2_氮氧化物浓度(mg/m3)', '机组2_烟尘浓度(mg/m3)', '机组2_流速(m/s)', '机组2_流量(m3/h)',\n '机组2_状态'],\n dtype='object')"
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},
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"execution_count": 10,
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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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"wxxs_daily.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": 11,
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"outputs": [
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{
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"data": {
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"text/plain": " days 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） \\\n0 2021-01-01 952 11032.5 5478.8 893 0.0 \n1 2021-01-02 1127 11180.5 6125.4 1061 0.0 \n2 2021-01-03 1051 11197.7 5717.6 1053 0.0 \n3 2021-01-04 1179 11146.6 6172.5 1237 0.0 \n4 2021-01-05 1142 10922.4 6053.3 1082 0.0 \n.. ... ... ... ... ... ... \n111 2021-04-22 0 0.0 0.0 910 0.0 \n112 2021-04-23 0 0.0 0.0 978 0.0 \n113 2021-04-24 0 0.0 0.0 1000 0.0 \n114 2021-04-25 0 0.0 0.0 912 0.0 \n115 2021-04-26 0 0.0 0.0 913 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） ... 机组2_烟尘浓度(mg/m3) \\\n0 4689.7 武乡西山发电有限责任公司 1845 11032.5 ... 2.25 \n1 5455.5 武乡西山发电有限责任公司 2188 11180.5 ... 2.37 \n2 4060.5 武乡西山发电有限责任公司 2104 11197.7 ... 2.38 \n3 5574.7 武乡西山发电有限责任公司 2416 11146.6 ... 2.39 \n4 6363.9 武乡西山发电有限责任公司 2224 10922.4 ... 2.42 \n.. ... ... ... ... ... ... \n111 4857.5 武乡西山发电有限责任公司 910 0.0 ... 1.35 \n112 4662.7 武乡西山发电有限责任公司 978 0.0 ... 1.56 \n113 5105.5 武乡西山发电有限责任公司 1000 0.0 ... 1.58 \n114 5133.1 武乡西山发电有限责任公司 912 0.0 ... 1.73 \n115 4615.6 武乡西山发电有限责任公司 913 0.0 ... 2.03 \n\n 机组2_流速(m/s) 机组2_流量(m3/h) 机组2_状态 c_smoke c_NO2 \\\n0 5.00 731609.00 正常运行 5.526793e+06 7.328620e+07 \n1 9.18 1326387.42 正常运行 7.281598e+06 9.776706e+07 \n2 9.85 1417186.13 正常运行 7.514610e+06 1.000303e+08 \n3 9.07 1304455.88 正常运行 7.176101e+06 9.310243e+07 \n4 9.71 1390566.88 正常运行 7.495997e+06 9.931573e+07 \n.. ... ... ... ... ... \n111 10.16 1382219.04 正常运行 1.865996e+06 4.958020e+07 \n112 10.63 1448026.88 正常运行 2.258922e+06 5.286746e+07 \n113 10.30 1413869.08 正常运行 2.233913e+06 5.222832e+07 \n114 10.02 1370190.96 正常运行 2.370430e+06 4.853216e+07 \n115 9.59 1316204.83 正常运行 2.671896e+06 4.660681e+07 \n\n c_SO2 flow r_O2 temperature \n0 4.749923e+07 2290111.96 NaN NaN \n1 6.054234e+07 2975016.92 NaN NaN \n2 6.075841e+07 3073868.76 NaN NaN \n3 5.473308e+07 2914952.34 NaN NaN \n4 6.013172e+07 3042896.96 NaN NaN \n.. ... ... ... ... \n111 2.526696e+07 1382219.04 NaN NaN \n112 2.089503e+07 1448026.88 NaN NaN \n113 2.267846e+07 1413869.08 NaN NaN \n114 1.964854e+07 1370190.96 NaN NaN \n115 2.409971e+07 1316204.83 NaN NaN \n\n[112 rows x 29 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_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>...</th>\n <th>机组2_烟尘浓度(mg/m3)</th>\n <th>机组2_流速(m/s)</th>\n <th>机组2_流量(m3/h)</th>\n <th>机组2_状态</th>\n <th>c_smoke</th>\n <th>c_NO2</th>\n <th>c_SO2</th>\n <th>flow</th>\n <th>r_O2</th>\n <th>temperature</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2021-01-01</td>\n <td>952</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</td>\n <td>11032.5</td>\n <td>...</td>\n <td>2.25</td>\n <td>5.00</td>\n <td>731609.00</td>\n <td>正常运行</td>\n <td>5.526793e+06</td>\n <td>7.328620e+07</td>\n <td>4.749923e+07</td>\n <td>2290111.96</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2021-01-02</td>\n <td>1127</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</td>\n <td>11180.5</td>\n <td>...</td>\n <td>2.37</td>\n <td>9.18</td>\n <td>1326387.42</td>\n <td>正常运行</td>\n <td>7.281598e+06</td>\n <td>9.776706e+07</td>\n <td>6.054234e+07</td>\n <td>2975016.92</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2021-01-03</td>\n <td>1051</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</td>\n <td>11197.7</td>\n <td>...</td>\n <td>2.38</td>\n <td>9.85</td>\n <td>1417186.13</td>\n <td>正常运行</td>\n <td>7.514610e+06</td>\n <td>1.000303e+08</td>\n <td>6.075841e+07</td>\n <td>3073868.76</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2021-01-04</td>\n <td>1179</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</td>\n <td>11146.6</td>\n <td>...</td>\n <td>2.39</td>\n <td>9.07</td>\n <td>1304455.88</td>\n <td>正常运行</td>\n <td>7.176101e+06</td>\n <td>9.310243e+07</td>\n <td>5.473308e+07</td>\n <td>2914952.34</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2021-01-05</td>\n <td>1142</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</td>\n <td>10922.4</td>\n <td>...</td>\n <td>2.42</td>\n <td>9.71</td>\n <td>1390566.88</td>\n <td>正常运行</td>\n <td>7.495997e+06</td>\n <td>9.931573e+07</td>\n <td>6.013172e+07</td>\n <td>3042896.96</td>\n <td>NaN</td>\n <td>NaN</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
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},
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"execution_count": 11,
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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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"wxxs_daily['c_smoke'] = wxxs_daily['机组1_流量(m3/h)'] * wxxs_daily['机组1_烟尘浓度(mg/m3)'] + wxxs_daily['机组2_流量(m3/h)'] * wxxs_daily['机组2_烟尘浓度(mg/m3)']\n",
|
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"wxxs_daily['c_NO2'] = wxxs_daily['机组1_流量(m3/h)'] * wxxs_daily['机组1_氮氧化物浓度(mg/m3)'] + wxxs_daily['机组2_流量(m3/h)'] * wxxs_daily['机组2_氮氧化物浓度(mg/m3)']\n",
|
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"wxxs_daily['c_SO2'] = wxxs_daily['机组1_流量(m3/h)'] * wxxs_daily['机组1_二氧化硫浓度(mg/m3)'] + wxxs_daily['机组2_流量(m3/h)'] * wxxs_daily['机组2_二氧化硫浓度(mg/m3)']\n",
|
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"wxxs_daily['flow'] = wxxs_daily['机组1_流量(m3/h)'] + wxxs_daily['机组2_流量(m3/h)']\n",
|
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"wxxs_daily['r_O2'] = np.nan\n",
|
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"wxxs_daily['temperature'] = np.nan\n",
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"wxxs_daily"
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"metadata": {
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"execution_count": 12,
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"outputs": [],
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"source": [
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"wxxs_daily['num_workers'] = (wxxs_daily[['机组1_状态', '机组2_状态']] == '正常运行').sum(axis=1).values"
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],
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"metadata": {
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"execution_count": 13,
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"outputs": [],
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"source": [
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"wxxs_daily_final = wxxs_daily[\n",
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" ['days', '企业名称', 'r_O2', 'temperature', '发电量（万千瓦时）', '供热量（吉焦）', 'c_smoke', 'c_NO2', 'c_SO2', 'flow', 'num_workers',\n",
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" '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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{
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"cell_type": "code",
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"execution_count": 14,
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"outputs": [
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{
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"data": {
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"text/plain": "(59, 28)"
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},
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"execution_count": 14,
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}
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],
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"source": [
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"hddj_daily.rename(columns={'Unnamed: 0': 'days'}, inplace=True)\n",
|
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"hddj_daily.shape"
|
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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": 15,
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"outputs": [
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{
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"data": {
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"text/plain": "Index(['days', '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）',\n '供热量_2（吉焦）', '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)',\n '机组1_SO2浓度(mg/m3)', '机组1_烟尘浓度(mg/m3)', '机组1_含氧量（%）', '机组1_温度（℃）',\n '机组1_烟气湿度（%）', '机组1_烟气压力（千帕）', '机组1_烟气流速（m/s）', '机组1_状态',\n '机组2_流量 （m3/h）', '机组2_NOx浓度(mg/m3)', '机组2_SO2浓度(mg/m3)',\n '机组2_烟尘浓度(mg/m3)', '机组2_含氧量（%）', '机组2_温度（℃）', '机组2_烟气湿度（%）',\n '机组2_烟气压力（千帕）', '机组2_烟气流速（m/s）', '机组2_状态'],\n dtype='object')"
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},
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"execution_count": 15,
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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.columns"
|
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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": 16,
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"outputs": [
|
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{
|
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"data": {
|
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"text/plain": " days 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） \\\n0 2022-01-01 494.20 30829 2861 536.50 324 \n1 2022-01-02 554.30 32122 2536 567.90 1008 \n2 2022-01-03 558.30 33451 2911 566.20 1296 \n3 2022-01-04 529.70 33179 3023 563.50 1248 \n4 2022-01-05 563.90 29731 3191 609.30 1362 \n5 2022-01-06 561.00 32505 3357 577.20 1236 \n6 2022-01-07 570.00 33189 3231 579.00 1035 \n7 2022-01-08 526.80 31881 2765 542.90 1323 \n8 2022-01-09 517.10 30799 2574 538.90 1305 \n9 2022-01-10 512.80 29277 2512 483.40 1335 \n10 2022-01-11 521.20 32460 2757 536.10 1299 \n11 2022-01-12 543.32 33593 3132 558.85 1368 \n12 2022-01-13 512.52 33326 2950 527.32 1398 \n13 2022-01-14 495.42 31417 2755 512.83 1536 \n14 2022-01-15 500.06 32434 2834 517.19 1242 \n15 2022-01-16 527.93 31986 3182 542.34 1116 \n16 2022-01-17 496.50 32268 3121 513.14 1362 \n17 2022-01-18 529.31 31814 3241 544.11 1305 \n18 2022-01-19 552.01 30414 3274 565.96 1161 \n19 2022-01-20 544.00 32416 3594 581.34 1329 \n20 2022-01-21 561.29 34300 3891 617.18 1383 \n21 2022-01-22 574.00 38342 4276 585.25 1368 \n22 2022-01-23 534.94 37444 3767 550.24 1242 \n23 2022-01-24 543.91 34539 3175 550.61 1362 \n24 2022-01-25 538.31 36753 3860 551.21 1131 \n25 2022-01-26 529.59 34148 3749 545.59 891 \n26 2022-01-27 525.88 33630 3725 537.33 777 \n27 2022-01-28 545.06 34181 3866 554.88 558 \n28 2022-01-29 522.68 30637 3395 544.47 399 \n29 2022-01-30 509.69 34254 3101 526.92 258 \n30 2022-01-31 516.51 32145 2985 528.13 210 \n31 2022-02-01 500.46 25687 2662 543.60 207 \n32 2022-02-02 477.03 29061 2821 534.40 186 \n33 2022-02-03 439.93 29598 2779 494.86 204 \n34 2022-02-04 452.76 28709 2523 493.99 282 \n35 2022-02-05 508.35 30133 2884 521.49 285 \n36 2022-02-06 485.41 27635 2902 503.13 363 \n37 2022-02-07 469.75 28259 2939 484.85 363 \n38 2022-02-08 452.87 26685 2556 481.92 603 \n39 2022-02-09 470.87 26037 2691 488.17 681 \n40 2022-02-10 490.75 25530 2901 508.89 591 \n41 2022-02-11 467.50 24359 2790 497.45 735 \n42 2022-02-12 446.31 24180 2766 474.04 621 \n43 2022-02-13 466.12 25274 3054 491.41 594 \n44 2022-02-14 464.96 29338 2533 491.96 651 \n45 2022-02-15 466.40 27394 2611 444.04 474 \n46 2022-02-16 493.26 27639 2905 420.10 1074 \n47 2022-02-17 495.53 30228 2894 469.32 1362 \n48 2022-02-18 524.59 28126 3030 531.28 1413 \n49 2022-02-19 466.49 27575
|
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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_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>...</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 <th>c_smoke</th>\n <th>c_NO2</th>\n <th>c_SO2</th>\n <th>flow</th>\n <th>r_O2</th>\n <th>temperature</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-01-01</td>\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>...</td>\n <td>14.332542</td>\n <td>-0.001208</td>\n <td>13.711958</td>\n <td>正常运行</td>\n <td>3.257517e+06</td>\n <td>3.804381e+07</td>\n <td>3.516073e+07</td>\n <td>2036233.80</td>\n <td>6.983896</td>\n <td>49.121292</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-01-02</td>\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>...</td>\n <td>13.511000</td>\n <td>-0.004167</td>\n <td>13.774167</td>\n <td>正常运行</td>\n <td>3.484952e+06</td>\n <td>4.221547e+07</td>\n <td>3.579036e+07</td>\n <td>2106074.55</td>\n <td>6.522833</td>\n <td>48.618542</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2022-01-03</td>\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>...</td>\n <td>13.971208</td>\n <td>-0.003500</td>\n <td>13.844875</td>\n <td>正常运行</td>\n <td>3.400451e+06</td>\n <td>4.260501e+07</td>\n <td>3.411403e+07</td>\n <td>2127569.40</td>\n <td>6.717021</td>\n <td>48.960354</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2022-01-04</td>\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>...</td>\n <td>13.916375</td>\n <td>-0.008208</td>\n <td>13.842125</td>\n <td>正常运行</td>\n <td>3.592141e+06</td>\n <td>3.998053e+07</td>\n <td>2.997909e+07</td>\n <td>2091564.90</td>\n <td>6.815604</td>\n <td>49.029333</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2022-01-05</td>\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>...</td>\n <td>14.428917</td>\n <td>0.005875</td>\n <td>14.311083</td>\n <td>正常运行</td>\n <td>3.586249e+06</td>\n <td>3.929008e+07</td>\n <td>3.131626e+07</td>\n <td>2137312.95</td
|
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},
|
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"execution_count": 16,
|
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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": [
|
|||
|
|
"hddj_daily['c_smoke'] = hddj_daily['机组1_流量 （m3/h）'] * hddj_daily['机组1_烟尘浓度(mg/m3)'] + hddj_daily['机组2_流量 （m3/h）'] * hddj_daily['机组2_烟尘浓度(mg/m3)']\n",
|
|||
|
|
"hddj_daily['c_NO2'] = hddj_daily['机组1_流量 （m3/h）'] * hddj_daily['机组1_NOx浓度(mg/m3)'] + hddj_daily['机组2_流量 （m3/h）'] * hddj_daily['机组2_NOx浓度(mg/m3)']\n",
|
|||
|
|
"hddj_daily['c_SO2'] = hddj_daily['机组1_流量 （m3/h）'] * hddj_daily['机组1_SO2浓度(mg/m3)'] + hddj_daily['机组2_流量 （m3/h）'] * hddj_daily['机组2_SO2浓度(mg/m3)']\n",
|
|||
|
|
"hddj_daily['flow'] = hddj_daily['机组1_流量 （m3/h）'] + hddj_daily['机组2_流量 （m3/h）']\n",
|
|||
|
|
"hddj_daily['r_O2'] = (hddj_daily['机组1_含氧量（%）'] + hddj_daily['机组2_含氧量（%）']) / 2\n",
|
|||
|
|
"hddj_daily['temperature'] = (hddj_daily['机组1_温度（℃）'] + hddj_daily['机组2_温度（℃）']) / 2\n",
|
|||
|
|
"hddj_daily"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"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,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"hddj_daily['发电量（万千瓦时）'] = hddj_daily['发电量_1（万千瓦时）'] + hddj_daily['发电量_2（万千瓦时）']\n",
|
|||
|
|
"hddj_daily['供热量（吉焦）'] = hddj_daily['供热量_1（吉焦）'] + hddj_daily['供热量_2（吉焦）']\n",
|
|||
|
|
"hddj_daily['燃料消耗量（吨）'] = hddj_daily['燃料消耗量_1（吨）'] = hddj_daily['燃料消耗量_2（吨）']\n",
|
|||
|
|
"hddj_daily['num_workers'] = (hddj_daily[['机组1_状态', '机组2_状态']] == '正常运行').sum(axis=1).values"
|
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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": 18,
|
|||
|
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"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"hddj_daily_final = hddj_daily[\n",
|
|||
|
|
" ['days', '企业名称', 'r_O2', 'temperature', '发电量（万千瓦时）', '供热量（吉焦）', 'c_smoke', 'c_NO2', 'c_SO2', 'flow', 'num_workers',\n",
|
|||
|
|
" '燃料消耗量（吨）']].copy()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"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": 19,
|
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"outputs": [
|
|||
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{
|
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"data": {
|
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"text/plain": "(31, 26)"
|
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|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
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|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"jtzh_daily.rename(columns={'Unnamed: 0': 'days'}, inplace=True)\n",
|
|||
|
|
"jtzh_daily['机组1_状态'] = (jtzh_daily['机组1_状态'] == '正常运行') * 1\n",
|
|||
|
|
"jtzh_daily['机组2_状态'] = (jtzh_daily['机组2_状态'] == '正常运行') * 1\n",
|
|||
|
|
"jtzh_daily.shape"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"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": 20,
|
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"outputs": [
|
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|
{
|
|||
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|
"data": {
|
|||
|
|
"text/plain": "Index(['days', '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）',\n '供热量_2（吉焦）', '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)',\n '机组1_SO2浓度(mg/m3)', '机组1_烟尘浓度(mg/m3)', '机组1_含氧量（%）', '机组1_温度（℃）',\n '机组1_烟气湿度（%）', '机组1_烟气流速（m/s）', '机组1_状态', '机组2_流量 （m3/h）',\n '机组2_NOx浓度(mg/m3)', '机组2_SO2浓度(mg/m3)', '机组2_烟尘浓度(mg/m3)', '机组2_含氧量（%）',\n '机组2_温度（℃）', '机组2_烟气湿度（%）', '机组2_烟气流速（m/s）', '机组2_状态'],\n dtype='object')"
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"jtzh_daily.columns"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
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|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "['机组1_NOx浓度(mg/m3)',\n '机组1_SO2浓度(mg/m3)',\n '机组1_烟尘浓度(mg/m3)',\n '机组2_NOx浓度(mg/m3)',\n '机组2_SO2浓度(mg/m3)',\n '机组2_烟尘浓度(mg/m3)']"
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"r_cols = [x for x in jtzh_daily.columns if '浓度' in x]\n",
|
|||
|
|
"r_cols"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for col in r_cols:\n",
|
|||
|
|
" if '机组1' in col:\n",
|
|||
|
|
" jtzh_daily[col] = jtzh_daily[col] * jtzh_daily['机组1_状态']\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" jtzh_daily[col] = jtzh_daily[col] * jtzh_daily['机组2_状态']"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " days 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） \\\n0 2022-05-01 444.630 0 1889 0.000 0 \n1 2022-05-02 516.594 0 2622 0.000 0 \n2 2022-05-03 410.316 0 2233 0.000 0 \n3 2022-05-04 421.908 0 2203 0.000 0 \n4 2022-05-05 486.318 0 2524 0.000 0 \n5 2022-05-06 457.542 0 2343 0.000 0 \n6 2022-05-07 451.140 0 2278 0.000 0 \n7 2022-05-08 484.986 0 1120 218.196 0 \n8 2022-05-09 0.000 0 0 444.756 0 \n9 2022-05-10 0.000 0 0 552.114 0 \n10 2022-05-11 0.000 0 0 481.542 0 \n11 2022-05-12 0.000 0 0 439.788 0 \n12 2022-05-13 0.000 0 0 495.702 0 \n13 2022-05-14 0.000 0 121 486.624 0 \n14 2022-05-15 159.936 0 2251 460.524 0 \n15 2022-05-16 516.192 0 2543 505.578 0 \n16 2022-05-17 432.300 0 2432 432.450 0 \n17 2022-05-18 508.680 0 2810 509.586 0 \n18 2022-05-19 516.066 0 2972 514.404 0 \n19 2022-05-20 517.356 0 2623 514.434 0 \n20 2022-05-21 521.454 0 2759 520.626 0 \n21 2022-05-22 504.798 0 2545 500.040 0 \n22 2022-05-23 587.400 0 3294 584.886 0 \n23 2022-05-24 515.964 0 2633 514.488 0 \n24 2022-05-25 485.346 0 2694 486.474 0 \n25 2022-05-26 503.502 0 2619 501.366 0 \n26 2022-05-27 470.340 0 2510 467.106 0 \n27 2022-05-28 508.644 0 2753 504.900 0 \n28 2022-05-29 460.536 0 1163 462.822 0 \n29 2022-05-30 8.610 0 0 528.960 0 \n30 2022-05-31 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) ... \\\n0 0 建投遵化热电有限责任公司 820965 12.952083 ... \n1 0 建投遵化热电有限责任公司 907380 14.641667 ... \n2 0 建投遵化热电有限责任公司 771360 14.414167 ... \n3 0 建投遵化热电有限责任公司 780390 14.922500 ... \n4 0 建投遵化热电有限责任公司 870075 14.102917 ... \n5 0 建投遵化热电有限责任公司 816300 14.321667 ... \n6 426 建投遵化热电有限责任公司 804405 16.310833 ... \n7 1873 建投遵化热电有限责任公司 845610 18.585417 ... \n8 2350 建投遵化热电有限责任公司 26370 0.000000 ... \n9 2636 建投遵化热电有限责任公司 269970 -0.000000 ... \n10 2432 建投遵化热电有限责任公司 163470 -0.000000 ... \n11 2099 建投遵化热电有限责任公司 27720 -0.000000 ... \n12 2340 建投遵化热电有限责任公司 50760 -0.000000 ... \n13 2737 建投遵化热电有限责任公司 48540 -0.000000 ... \n14 2461 <20>
|
|||
|
|
"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_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>...</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 <th>c_smoke</th>\n <th>c_NO2</th>\n <th>c_SO2</th>\n <th>flow</th>\n <th>num_workers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-05-01</td>\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</td>\n <td>12.952083</td>\n <td>...</td>\n <td>20.512500</td>\n <td>13.150000</td>\n <td>0.000000</td>\n <td>0.630417</td>\n <td>0</td>\n <td>1.467475e+06</td>\n <td>1.063321e+07</td>\n <td>1.055453e+07</td>\n <td>885255</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-05-02</td>\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</td>\n <td>14.641667</td>\n <td>...</td>\n <td>20.416667</td>\n <td>15.525000</td>\n <td>0.000000</td>\n <td>0.704583</td>\n <td>0</td>\n <td>1.558803e+06</td>\n <td>1.328556e+07</td>\n <td>1.074905e+07</td>\n <td>978615</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2022-05-03</td>\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</td>\n <td>14.414167</td>\n <td>...</td>\n <td>19.479167</td>\n <td>22.129167</td>\n <td>0.200000</td>\n <td>0.722917</td>\n <td>0</td>\n <td>1.427659e+06</td>\n <td>1.111851e+07</td>\n <td>9.591219e+06</td>\n <td>842730</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2022-05-04</td>\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</td>\n <td>14.922500</td>\n <td>...</td>\n <td>20.095833</td>\n <td>26.258333</td>\n <td>0.016667</td>\n <td>0.757500</td>\n <td>0</td>\n <td>1.499975e+06</td>\n <td>1.164537e+07</td>\n <td>1.007516e+07</td>\n <td>854175</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2022-05-05</td>\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</td>\n <td>14.102917</td>\n <td>...</td>\n <td>20.150000</td>\n <td>23.062500</td>\n <td>0.212500</td>\n <td>0.750000</td>\n <td>0</td>\n <td>1.724199e+06</td>\n <td>1.227060e+07</td>\n <td>1.193453e+07</td>\n <td>943680</td>\n <td>1</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2022-05-06</td>\n <td>457.542</td>\n <td>0</td>\n <td>2343</td>\n <td>0.000</td>\n <td>0</td>\n <td>
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"jtzh_daily['c_smoke'] = jtzh_daily['机组1_流量 （m3/h）'] * jtzh_daily['机组1_烟尘浓度(mg/m3)'] + jtzh_daily['机组2_流量 （m3/h）'] * jtzh_daily['机组2_烟尘浓度(mg/m3)']\n",
|
|||
|
|
"jtzh_daily['c_NO2'] = jtzh_daily['机组1_流量 （m3/h）'] * jtzh_daily['机组1_NOx浓度(mg/m3)'] + jtzh_daily['机组2_流量 （m3/h）'] * jtzh_daily['机组2_NOx浓度(mg/m3)']\n",
|
|||
|
|
"jtzh_daily['c_SO2'] = jtzh_daily['机组1_流量 （m3/h）'] * jtzh_daily['机组1_SO2浓度(mg/m3)'] + jtzh_daily['机组2_流量 （m3/h）'] * jtzh_daily['机组2_SO2浓度(mg/m3)']\n",
|
|||
|
|
"jtzh_daily['flow'] = jtzh_daily['机组1_流量 （m3/h）'] + jtzh_daily['机组2_流量 （m3/h）']\n",
|
|||
|
|
"jtzh_daily['num_workers'] = jtzh_daily['机组1_状态'] + jtzh_daily['机组2_状态']\n",
|
|||
|
|
"jtzh_daily"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"text/plain": " days 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） \\\n0 2022-05-01 444.630 0 1889 0.000 0 \n1 2022-05-02 516.594 0 2622 0.000 0 \n2 2022-05-03 410.316 0 2233 0.000 0 \n3 2022-05-04 421.908 0 2203 0.000 0 \n4 2022-05-05 486.318 0 2524 0.000 0 \n5 2022-05-06 457.542 0 2343 0.000 0 \n6 2022-05-07 451.140 0 2278 0.000 0 \n7 2022-05-08 484.986 0 1120 218.196 0 \n8 2022-05-09 0.000 0 0 444.756 0 \n9 2022-05-10 0.000 0 0 552.114 0 \n10 2022-05-11 0.000 0 0 481.542 0 \n11 2022-05-12 0.000 0 0 439.788 0 \n12 2022-05-13 0.000 0 0 495.702 0 \n13 2022-05-14 0.000 0 121 486.624 0 \n14 2022-05-15 159.936 0 2251 460.524 0 \n15 2022-05-16 516.192 0 2543 505.578 0 \n16 2022-05-17 432.300 0 2432 432.450 0 \n17 2022-05-18 508.680 0 2810 509.586 0 \n18 2022-05-19 516.066 0 2972 514.404 0 \n19 2022-05-20 517.356 0 2623 514.434 0 \n20 2022-05-21 521.454 0 2759 520.626 0 \n21 2022-05-22 504.798 0 2545 500.040 0 \n22 2022-05-23 587.400 0 3294 584.886 0 \n23 2022-05-24 515.964 0 2633 514.488 0 \n24 2022-05-25 485.346 0 2694 486.474 0 \n25 2022-05-26 503.502 0 2619 501.366 0 \n26 2022-05-27 470.340 0 2510 467.106 0 \n27 2022-05-28 508.644 0 2753 504.900 0 \n28 2022-05-29 460.536 0 1163 462.822 0 \n29 2022-05-30 8.610 0 0 528.960 0 \n30 2022-05-31 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) ... \\\n0 0 建投遵化热电有限责任公司 820965 12.952083 ... \n1 0 建投遵化热电有限责任公司 907380 14.641667 ... \n2 0 建投遵化热电有限责任公司 771360 14.414167 ... \n3 0 建投遵化热电有限责任公司 780390 14.922500 ... \n4 0 建投遵化热电有限责任公司 870075 14.102917 ... \n5 0 建投遵化热电有限责任公司 816300 14.321667 ... \n6 426 建投遵化热电有限责任公司 804405 16.310833 ... \n7 1873 建投遵化热电有限责任公司 845610 18.585417 ... \n8 2350 建投遵化热电有限责任公司 26370 0.000000 ... \n9 2636 建投遵化热电有限责任公司 269970 -0.000000 ... \n10 2432 建投遵化热电有限责任公司 163470 -0.000000 ... \n11 2099 建投遵化热电有限责任公司 27720 -0.000000 ... \n12 2340 建投遵化热电有限责任公司 50760 -0.000000 ... \n13 2737 建投遵化热电有限责任公司 48540 -0.000000 ... \n14 2461 <20>
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"def cal_mixture(x):\n",
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" s1, s2, t1, t2 = x\n",
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" if s1 ^ s2 == 1:\n",
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"text/plain": " days 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） \\\n0 2022-05-01 444.630 0 1889 0.000 0 \n1 2022-05-02 516.594 0 2622 0.000 0 \n2 2022-05-03 410.316 0 2233 0.000 0 \n3 2022-05-04 421.908 0 2203 0.000 0 \n4 2022-05-05 486.318 0 2524 0.000 0 \n5 2022-05-06 457.542 0 2343 0.000 0 \n6 2022-05-07 451.140 0 2278 0.000 0 \n7 2022-05-08 484.986 0 1120 218.196 0 \n8 2022-05-09 0.000 0 0 444.756 0 \n9 2022-05-10 0.000 0 0 552.114 0 \n10 2022-05-11 0.000 0 0 481.542 0 \n11 2022-05-12 0.000 0 0 439.788 0 \n12 2022-05-13 0.000 0 0 495.702 0 \n13 2022-05-14 0.000 0 121 486.624 0 \n14 2022-05-15 159.936 0 2251 460.524 0 \n15 2022-05-16 516.192 0 2543 505.578 0 \n16 2022-05-17 432.300 0 2432 432.450 0 \n17 2022-05-18 508.680 0 2810 509.586 0 \n18 2022-05-19 516.066 0 2972 514.404 0 \n19 2022-05-20 517.356 0 2623 514.434 0 \n20 2022-05-21 521.454 0 2759 520.626 0 \n21 2022-05-22 504.798 0 2545 500.040 0 \n22 2022-05-23 587.400 0 3294 584.886 0 \n23 2022-05-24 515.964 0 2633 514.488 0 \n24 2022-05-25 485.346 0 2694 486.474 0 \n25 2022-05-26 503.502 0 2619 501.366 0 \n26 2022-05-27 470.340 0 2510 467.106 0 \n27 2022-05-28 508.644 0 2753 504.900 0 \n28 2022-05-29 460.536 0 1163 462.822 0 \n29 2022-05-30 8.610 0 0 528.960 0 \n30 2022-05-31 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) ... \\\n0 0 建投遵化热电有限责任公司 820965 12.952083 ... \n1 0 建投遵化热电有限责任公司 907380 14.641667 ... \n2 0 建投遵化热电有限责任公司 771360 14.414167 ... \n3 0 建投遵化热电有限责任公司 780390 14.922500 ... \n4 0 建投遵化热电有限责任公司 870075 14.102917 ... \n5 0 建投遵化热电有限责任公司 816300 14.321667 ... \n6 426 建投遵化热电有限责任公司 804405 16.310833 ... \n7 1873 建投遵化热电有限责任公司 845610 18.585417 ... \n8 2350 建投遵化热电有限责任公司 26370 0.000000 ... \n9 2636 建投遵化热电有限责任公司 269970 -0.000000 ... \n10 2432 建投遵化热电有限责任公司 163470 -0.000000 ... \n11 2099 建投遵化热电有限责任公司 27720 -0.000000 ... \n12 2340 建投遵化热电有限责任公司 50760 -0.000000 ... \n13 2737 建投遵化热电有限责任公司 48540 -0.000000 ... \n14 2461 <20>
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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_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>...</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 <th>c_smoke</th>\n <th>c_NO2</th>\n <th>c_SO2</th>\n <th>flow</th>\n <th>num_workers</th>\n <th>temperature</th>\n <th>r_O2</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2022-05-01</td>\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</td>\n <td>12.952083</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.630417</td>\n <td>0</td>\n <td>1.467475e+06</td>\n <td>1.063321e+07</td>\n <td>1.055453e+07</td>\n <td>885255</td>\n <td>1</td>\n <td>52.320833</td>\n <td>7.795833</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2022-05-02</td>\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</td>\n <td>14.641667</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.704583</td>\n <td>0</td>\n <td>1.558803e+06</td>\n <td>1.328556e+07</td>\n <td>1.074905e+07</td>\n <td>978615</td>\n <td>1</td>\n <td>53.145833</td>\n <td>7.295833</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2022-05-03</td>\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</td>\n <td>14.414167</td>\n <td>...</td>\n <td>0.200000</td>\n <td>0.722917</td>\n <td>0</td>\n <td>1.427659e+06</td>\n <td>1.111851e+07</td>\n <td>9.591219e+06</td>\n <td>842730</td>\n <td>1</td>\n <td>51.841667</td>\n <td>7.650000</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2022-05-04</td>\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</td>\n <td>14.922500</td>\n <td>...</td>\n <td>0.016667</td>\n <td>0.757500</td>\n <td>0</td>\n <td>1.499975e+06</td>\n <td>1.164537e+07</td>\n <td>1.007516e+07</td>\n <td>854175</td>\n <td>1</td>\n <td>51.808333</td>\n <td>7.462500</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2022-05-05</td>\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</td>\n <td>14.102917</td>\n <td>...</td>\n <td>0.212500</td>\n <td>0.750000</td>\n <td>0</td>\n <td>1.724199e+06</td>\n <td>1.227060e+07</td>\n <td>1.193453e+07</td>\n <td>943680</td>\n <td>1</td>\n <td>52.354167</td>\n <td>7.195833</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2022-05-06</td>\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 <td>建投遵化热电<E783AD>
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"source": [
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"jtzh_daily['temperature'] = jtzh_daily.apply(lambda x: cal_mixture(x[['机组1_状态', '机组2_状态', '机组1_温度（℃）', '机组2_温度（℃）']]), axis=1)\n",
|
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|
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"jtzh_daily['r_O2'] = jtzh_daily.apply(lambda x: cal_mixture(x[['机组1_状态', '机组2_状态', '机组1_含氧量（%）', '机组2_含氧量（%）']]), axis=1)\n",
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"jtzh_daily"
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"execution_count": 27,
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"outputs": [],
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"source": [
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"jtzh_daily['发电量（万千瓦时）'] = jtzh_daily['发电量_1（万千瓦时）'] + jtzh_daily['发电量_2（万千瓦时）']\n",
|
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|
|
"jtzh_daily['供热量（吉焦）'] = jtzh_daily['供热量_1（吉焦）'] + jtzh_daily['供热量_2（吉焦）']\n",
|
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|
|
"jtzh_daily['燃料消耗量（吨）'] = jtzh_daily['燃料消耗量_1（吨）'] = jtzh_daily['燃料消耗量_2（吨）']\n",
|
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"jtzh_daily['num_workers'] = jtzh_daily['机组1_状态'] + jtzh_daily['机组2_状态']"
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"jtzh_daily_final = jtzh_daily[['days', '企业名称', 'r_O2', 'temperature', '发电量（万千瓦时）', '供热量（吉焦）', 'c_smoke', 'c_NO2', 'c_SO2', 'flow', 'num_workers',\n",
|
|||
|
|
" '燃料消耗量（吨）']].copy()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"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": 29,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"all_daily_data = pd.concat([zjxz_daily_final, wxxs_daily_final, hddj_daily_final, jtzh_daily_final])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
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"pycharm": {
|
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|
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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": 30,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"all_unit_daily_data = pd.concat([zjxz_daily, wxxs_daily, hddj_daily, jtzh_daily])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
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"collapsed": false,
|
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{
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"cell_type": "code",
|
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"execution_count": 31,
|
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"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"unit_features = pd.read_excel('data/机组特征表.xlsx', header=None).T"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
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"collapsed": false,
|
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|
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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": 32,
|
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"outputs": [
|
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{
|
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"data": {
|
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|
|
"text/plain": " 企业名称 地址 省份 经度 纬度 \\\n1 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″ \n2 武乡西山发电有限责任公司 山西省武乡县丰州镇下城村 山西省 112°51′11″ 36°50′0″ \n3 国电电力邯郸东郊热电有限责任公司 河北肥乡经济开发区光明西路11号 河北省 114°39'0\" 36°34'0\" \n4 建投遵化热电有限责任公司 河北省遵化市新店子镇程庄子村 河北省 117°58'40\" 40°4'33\" \n\n 烟囱高度（m） 脱硝工艺 脱硝剂名称 脱硝设备数量 脱硫工艺 ... 发电机组1_单机容量（MW） \\\n1 80 SNCR SCR 氨水 3 石灰石-石膏湿法 ... 15 \n2 240 SCR 尿素 2 石灰石--石膏湿法 ... 600 \n3 NaN 采用高效低氮燃烧器＋SCR NaN NaN 石灰石-石膏湿法 ... 350 \n4 NaN 采用高效低氮燃烧器＋SCR NaN NaN 石灰石-石膏湿法 ... 350 \n\n 发电机组1_投产日期 发电机组1_汽轮机类型 发电机组1_压力参数 发电机组1_冷却方式 发电机组2_单机容量（MW） \\\n1 2014-07-01 00:00:00 背压式 高压 水冷-闭式循环 15 \n2 2006-10-07 00:00:00 抽凝式 亚临界 空冷-直接空冷 600 \n3 2019-06-08 00:00:00 抽凝式 超超临界 水冷-闭式循环 350 \n4 2019-07-09 00:00:00 抽凝式 超临界 水冷-闭式循环 350 \n\n 发电机组2_投产日期 发电机组2_汽轮机类型 发电机组2_压力参数 发电机组2_冷却方式 \n1 2018-08-01 00:00:00 抽背式 高压 水冷-闭式循环 \n2 2007-01-07 00:00:00 抽凝式 亚临界 空冷-直接空冷 \n3 2019-06-08 00:00:00 抽凝式 超超临界 水冷-闭式循环 \n4 2020-05-02 00:00:00 抽凝式 超临界 水冷-闭式循环 \n\n[4 rows x 33 columns]",
|
|||
|
|
"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>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>脱硫工艺</th>\n <th>...</th>\n <th>发电机组1_单机容量（MW）</th>\n <th>发电机组1_投产日期</th>\n <th>发电机组1_汽轮机类型</th>\n <th>发电机组1_压力参数</th>\n <th>发电机组1_冷却方式</th>\n <th>发电机组2_单机容量（MW）</th>\n <th>发电机组2_投产日期</th>\n <th>发电机组2_汽轮机类型</th>\n <th>发电机组2_压力参数</th>\n <th>发电机组2_冷却方式</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\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>石灰石-石膏湿法</td>\n <td>...</td>\n <td>15</td>\n <td>2014-07-01 00:00:00</td>\n <td>背压式</td>\n <td>高压</td>\n <td>水冷-闭式循环</td>\n <td>15</td>\n <td>2018-08-01 00:00:00</td>\n <td>抽背式</td>\n <td>高压</td>\n <td>水冷-闭式循环</td>\n </tr>\n <tr>\n <th>2</th>\n <td>武乡西山发电有限责任公司</td>\n <td>山西省武乡县丰州镇下城村</td>\n <td>山西省</td>\n <td>112°51′11″</td>\n <td>36°50′0″</td>\n <td>240</td>\n <td>SCR</td>\n <td>尿素</td>\n <td>2</td>\n <td>石灰石--石膏湿法</td>\n <td>...</td>\n <td>600</td>\n <td>2006-10-07 00:00:00</td>\n <td>抽凝式</td>\n <td>亚临界</td>\n <td>空冷-直接空冷</td>\n <td>600</td>\n <td>2007-01-07 00:00:00</td>\n <td>抽凝式</td>\n <td>亚临界</td>\n <td>空冷-直接空冷</td>\n </tr>\n <tr>\n <th>3</th>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>河北肥乡经济开发区光明西路11号</td>\n <td>河北省</td>\n <td>114°39'0\"</td>\n <td>36°34'0\"</td>\n <td>NaN</td>\n <td>采用高效低氮燃烧器＋SCR</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>石灰石-石膏湿法</td>\n <td>...</td>\n <td>350</td>\n <td>2019-06-08 00:00:00</td>\n <td>抽凝式</td>\n <td>超超临界</td>\n <td>水冷-闭式循环</td>\n <td>350</td>\n <td>2019-06-08 00:00:00</td>\n <td>抽凝式</td>\n <td>超超临界</td>\n <td>水冷-闭式循环</td>\n </tr>\n <tr>\n <th>4</th>\n <td>建投遵化热电有限责任公司</td>\n <td>河北省遵化市新店子镇程庄子村</td>\n <td>河北省</td>\n <td>117°58'40\"</td>\n <td>40°4'33\"</td>\n <td>NaN</td>\n <td>采用高效低氮燃烧器＋SCR</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>石灰石-石膏湿法</td>\n <td>...</td>\n <td>350</td>\n <td>2019-07-09 00:00:00</td>\n <td>抽凝式</td>\n <td>超临界</td>\n <td>水冷-闭式循环</td>\n <td>350</td>\n <td>2020-05-02 00:00:00</td>\n <td>抽凝式</td>\n <td>超临界</td>\n <td>水冷-闭式循环</td>\n </tr>\n </tbody>\n</table>\n<p>4 rows × 33 columns</p>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"unit_features = unit_features.rename(columns=unit_features.iloc[0]).drop(unit_features.index[0])\n",
|
|||
|
|
"unit_features.head()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "1 水冷-闭式循环\n2 空冷-直接空冷\n3 水冷-闭式循环\n4 水冷-闭式循环\nName: 发电机组1_冷却方式, dtype: object"
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"unit_features['发电机组1_冷却方式']"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"unit_features_used = unit_features[['企业名称', '生产设备类型', '燃料类型', '低位发热量（GJ/t）']].copy()\n",
|
|||
|
|
"unit_features_used['汽轮机类型'] = unit_features['发电机组1_汽轮机类型']\n",
|
|||
|
|
"unit_features_used['冷却方式'] = unit_features['发电机组1_冷却方式']\n",
|
|||
|
|
"unit_features_used['额定蒸发量'] = unit_features['锅炉1额定蒸发量 t/h'] + unit_features['锅炉2额定蒸发量 t/h']\n",
|
|||
|
|
"unit_features_used['压力参数'] = unit_features['发电机组1_压力参数']\n",
|
|||
|
|
"unit_features_used['总容量'] = unit_features['发电机组1_单机容量（MW）'] + unit_features['发电机组2_单机容量（MW）']"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": " 企业名称 生产设备类型 燃料类型 低位发热量（GJ/t） 汽轮机类型 冷却方式 额定蒸发量 \\\n1 浙江秀舟热电有限公司 高温高压循环流化床锅炉 中高挥发分烟煤 20.501 背压式 水冷-闭式循环 230 \n2 武乡西山发电有限责任公司 煤粉锅炉 褐煤 18.643 抽凝式 空冷-直接空冷 4160 \n3 国电电力邯郸东郊热电有限责任公司 煤粉锅炉 低挥发分烟煤 19.087 抽凝式 水冷-闭式循环 2250 \n4 建投遵化热电有限责任公司 煤粉锅炉 一般烟煤 14.682 抽凝式 水冷-闭式循环 2344 \n\n 压力参数 总容量 \n1 高压 30 \n2 亚临界 1200 \n3 超超临界 700 \n4 超临界 700 ",
|
|||
|
|
"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>企业名称</th>\n <th>生产设备类型</th>\n <th>燃料类型</th>\n <th>低位发热量（GJ/t）</th>\n <th>汽轮机类型</th>\n <th>冷却方式</th>\n <th>额定蒸发量</th>\n <th>压力参数</th>\n <th>总容量</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>浙江秀舟热电有限公司</td>\n <td>高温高压循环流化床锅炉</td>\n <td>中高挥发分烟煤</td>\n <td>20.501</td>\n <td>背压式</td>\n <td>水冷-闭式循环</td>\n <td>230</td>\n <td>高压</td>\n <td>30</td>\n </tr>\n <tr>\n <th>2</th>\n <td>武乡西山发电有限责任公司</td>\n <td>煤粉锅炉</td>\n <td>褐煤</td>\n <td>18.643</td>\n <td>抽凝式</td>\n <td>空冷-直接空冷</td>\n <td>4160</td>\n <td>亚临界</td>\n <td>1200</td>\n </tr>\n <tr>\n <th>3</th>\n <td>国电电力邯郸东郊热电有限责任公司</td>\n <td>煤粉锅炉</td>\n <td>低挥发分烟煤</td>\n <td>19.087</td>\n <td>抽凝式</td>\n <td>水冷-闭式循环</td>\n <td>2250</td>\n <td>超超临界</td>\n <td>700</td>\n </tr>\n <tr>\n <th>4</th>\n <td>建投遵化热电有限责任公司</td>\n <td>煤粉锅炉</td>\n <td>一般烟煤</td>\n <td>14.682</td>\n <td>抽凝式</td>\n <td>水冷-闭式循环</td>\n <td>2344</td>\n <td>超临界</td>\n <td>700</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"unit_features_used"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"final_data = all_daily_data.merge(unit_features_used, how='left', on='企业名称')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
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|
|
}
|
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|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import datetime as dt"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def cal_timedelta(x):\n",
|
|||
|
|
" date = dt.datetime.strptime(x, '%Y-%m-%d')\n",
|
|||
|
|
" date = dt.date(date.year, date.month, date.day)\n",
|
|||
|
|
" start_date = dt.date(date.year, 1, 1)\n",
|
|||
|
|
" time_delta = (date - start_date).days\n",
|
|||
|
|
" return time_delta"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"final_data['day_of_year'] = final_data.days.apply(cal_timedelta)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 2
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython2",
|
|||
|
|
"version": "2.7.6"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 0
|
|||
|
|
}
|