701 lines
60 KiB
Plaintext
701 lines
60 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"
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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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"source": [
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"import numpy as np\n",
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"import time"
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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": "markdown",
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"source": [
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"$E = E_{燃烧} + E_{脱硫} + E_{电}$ \\\n",
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"$E_{燃烧} = \\sum_i (AD_i\\times EF_i)$ \\\n",
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"$AD_i = FC_i \\times NCV_i \\times 10^{-6}$ 消耗量*低位热值\n",
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"$EF_i = CC_i \\times OF_i \\times \\frac{44}{12}$ 单位热值含碳量 * 碳氧化率"
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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",
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"execution_count": 3,
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"outputs": [],
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"source": [
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"def cal_ad(fc, ncv):\n",
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" return fc * ncv * 1e-6"
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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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"source": [
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"def cal_ef(cc, of):\n",
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" return cc * of * 44 / 12"
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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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"cc_map = {\n",
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" \"无烟煤\": 27.49,\n",
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" \"烟煤\": 26.18,\n",
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" \"褐煤\": 27.97,\n",
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" \"贫煤\": 26.0,\n",
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"}"
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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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"data = pd.read_excel('./total_data.xlsx')"
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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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"data['入炉煤低位热值(kJ/kg)'] = data['入炉煤低位热值(kJ/kg)'].apply(lambda x: x*1000 if x < 100 else x*1)\n",
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"data['燃煤灰份Aar(%)'] = data['燃煤灰份Aar(%)'].apply(lambda x: x / 1000 if x > 10000 else x*1)\n",
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"data['燃煤挥发份Var(%)'] = data['燃煤挥发份Var(%)'].apply(lambda x: x / 1000 if x > 10000 else x*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": 8,
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"outputs": [],
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"source": [
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"def cal_ctype(ncv, vdaf, ash):\n",
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" if pd.isna(ncv) or pd.isna(vdaf) or pd.isna(ash):\n",
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" return pd.NA\n",
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" else:\n",
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" if vdaf / (1 - ash/100 - 0.07196) < 10:\n",
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" return '无烟煤'\n",
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" elif vdaf / (1 - ash/100 - 0.06825) < 20:\n",
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" return '贫煤'\n",
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" elif vdaf / (1 - ash/100 - 0.08679) < 37 and ncv > 16730:\n",
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" return '烟煤'\n",
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" elif ncv < 16730:\n",
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" return '褐煤'\n",
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" else:\n",
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" return '烟煤'"
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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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"source": [
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"data['煤种'] = data.apply(lambda x: cal_ctype(x['入炉煤低位热值(kJ/kg)'], x['燃煤挥发份Var(%)'], x['燃煤灰份Aar(%)']), axis=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": 10,
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"outputs": [],
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"source": [
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"data = data[~data['煤种'].isna()].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": 11,
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"outputs": [],
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"source": [
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"data = data[data['入炉煤低位热值(kJ/kg)']> 8000].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": 12,
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"outputs": [],
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"source": [
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"data = data[~((data['发电用标煤量（t）'].isna())|(data['发电用标煤量（t）']==0))].copy()\n",
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"data = data[~((data['发电用标煤量（t）'].isna())|(data['发电用标煤量（t）']==0))].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": 13,
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"outputs": [
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{
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"data": {
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"text/plain": "['出力系数(%).1', '出力系数(%).2', '化学系统耗电率 (%).1', '额再热蒸汽温度 (℃).1']"
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},
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"execution_count": 13,
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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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"drop_cols = [x for x in data.columns if '.1' in x or '.2' in x]\n",
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"drop_cols"
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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": 14,
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"outputs": [],
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"source": [
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"data.drop(columns=drop_cols, 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": 15,
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"outputs": [],
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"source": [
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"data['工业供热量'].fillna(0, inplace=True)\n",
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"data['采暖供热量'].fillna(0, 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": 16,
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"outputs": [
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{
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"data": {
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"text/plain": "array([ 56, 96, 122, ..., 16724, 16725, 16837], dtype=int64)"
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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": [
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"drop_index = data[(data['发电量(万kWh)']<10000)|(data['总供热量']<=10000)|(data['供热用标煤量（t）']==0)].index.values\n",
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"drop_index"
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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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"data.drop(index=drop_index, inplace=True)\n",
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"data.reset_index(inplace=True, drop=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": 18,
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"outputs": [],
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"source": [
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"data['发电碳排放因子(kg/kWh)'] = data.apply(lambda x: (cal_ad(x['发电用标煤量（t）'], x['入炉煤低位热值(kJ/kg)']) * cal_ef(cc_map.get(x['煤种']), 0.98))/x['发电量(万kWh)']/10, axis=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": 19,
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"outputs": [],
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"source": [
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"data['供热碳排放因子(kg/MJ)'] = data.apply(lambda x:(cal_ad(x['供热用标煤量（t）'], x['入炉煤低位热值(kJ/kg)']) * cal_ef(cc_map.get(x['煤种']), 0.98))/x['总供热量'] if x['总供热量'] != 0 else 0, axis=1)"
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],
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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": "code",
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"execution_count": 20,
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"outputs": [
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{
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"data": {
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"text/plain": " 总供热量 供热碳排放因子(kg/MJ)\n0 8.294556e+05 0.076843\n1 8.789928e+04 0.077676\n2 8.481764e+05 0.074823\n3 4.473469e+05 0.081628\n4 7.157164e+05 0.081103\n... ... ...\n7252 1.532303e+06 0.078776\n7253 2.147545e+06 0.076622\n7254 2.131207e+06 0.074772\n7255 3.039811e+06 0.091482\n7256 3.039813e+06 0.091483\n\n[7257 rows x 2 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>总供热量</th>\n <th>供热碳排放因子(kg/MJ)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>8.294556e+05</td>\n <td>0.076843</td>\n </tr>\n <tr>\n <th>1</th>\n <td>8.789928e+04</td>\n <td>0.077676</td>\n </tr>\n <tr>\n <th>2</th>\n <td>8.481764e+05</td>\n <td>0.074823</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4.473469e+05</td>\n <td>0.081628</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.157164e+05</td>\n <td>0.081103</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>7252</th>\n <td>1.532303e+06</td>\n <td>0.078776</td>\n </tr>\n <tr>\n <th>7253</th>\n <td>2.147545e+06</td>\n <td>0.076622</td>\n </tr>\n <tr>\n <th>7254</th>\n <td>2.131207e+06</td>\n <td>0.074772</td>\n </tr>\n <tr>\n <th>7255</th>\n <td>3.039811e+06</td>\n <td>0.091482</td>\n </tr>\n <tr>\n <th>7256</th>\n <td>3.039813e+06</td>\n <td>0.091483</td>\n </tr>\n </tbody>\n</table>\n<p>7257 rows × 2 columns</p>\n</div>"
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"source": [
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"data[['总供热量', '供热碳排放因子(kg/MJ)']]"
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"execution_count": 21,
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{
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"data": {
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"text/plain": "126 0.460130\n127 0.480800\n128 0.493493\n129 0.487034\n130 0.475439\n131 0.481355\n132 0.450568\n133 0.454952\n134 0.460595\n135 0.478949\n136 0.486923\n137 0.483407\n138 0.488134\n139 0.471598\n140 0.450577\n141 0.461044\nName: 发电碳排放因子(kg/kWh), dtype: float64"
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},
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"execution_count": 21,
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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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"data[data['电厂名称']=='华电内蒙古能源有限公司包头发电分公司']['发电碳排放因子(kg/kWh)']"
|
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],
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"metadata": {
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"collapsed": false,
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"name": "#%%\n"
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{
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"cell_type": "code",
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"execution_count": 22,
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"outputs": [
|
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{
|
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"data": {
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"text/plain": " plant longitude latitude altitude\n0 万方发电厂（焦作爱依斯万方电力有限公司) 113.381649 35.255622 88.0\n1 三河发电有限责任公司 116.860260 39.953617 27.0\n2 上海上电漕泾发电有限公司 121.407593 30.765242 4.0\n3 上海吴泾发电有限责任公司 121.471140 31.065113 3.0\n4 上海吴泾第二发电有限责任公司 121.471340 31.062532 4.0",
|
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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>plant</th>\n <th>longitude</th>\n <th>latitude</th>\n <th>altitude</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>万方发电厂（焦作爱依斯万方电力有限公司)</td>\n <td>113.381649</td>\n <td>35.255622</td>\n <td>88.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>三河发电有限责任公司</td>\n <td>116.860260</td>\n <td>39.953617</td>\n <td>27.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>上海上电漕泾发电有限公司</td>\n <td>121.407593</td>\n <td>30.765242</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>上海吴泾发电有限责任公司</td>\n <td>121.471140</td>\n <td>31.065113</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>上海吴泾第二发电有限责任公司</td>\n <td>121.471340</td>\n <td>31.062532</td>\n <td>4.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
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|
},
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"execution_count": 22,
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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": [
|
|||
|
|
"loc_data = pd.read_csv('./电厂机组地理信息.csv')\n",
|
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|
|
"loc_data.head()"
|
|||
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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": 23,
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"outputs": [],
|
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"source": [
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"loc_data.rename(columns={'plant':'电厂名称'}, inplace=True)"
|
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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": 24,
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"outputs": [],
|
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"source": [
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"data = data.merge(loc_data, how='left', on='电厂名称')"
|
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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": 25,
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"outputs": [
|
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{
|
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|
"data": {
|
|||
|
|
"text/plain": "Index(['发电碳排放因子(kg/kWh)', '供热碳排放因子(kg/MJ)', 'longitude', 'latitude',\n 'altitude'],\n dtype='object')"
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
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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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],
|
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"source": [
|
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|
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"data.columns[-5:]"
|
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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",
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"uni = data.groupby(['电厂名称', '机组编号'])['铭牌容量 (MW)'].unique().to_frame()"
|
|||
|
|
],
|
|||
|
|
"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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|
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{
|
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|
"cell_type": "code",
|
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|
"execution_count": 27,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"uni['len'] = uni['铭牌容量 (MW)'].apply(len)"
|
|||
|
|
],
|
|||
|
|
"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": 28,
|
|||
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|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "Empty DataFrame\nColumns: [铭牌容量 (MW), len]\nIndex: []",
|
|||
|
|
"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></th>\n <th>铭牌容量 (MW)</th>\n <th>len</th>\n </tr>\n <tr>\n <th>电厂名称</th>\n <th>机组编号</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n </tbody>\n</table>\n</div>"
|
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|
|
},
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"metadata": {},
|
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|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"uni[uni.len==2]"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
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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",
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"data = data[data['供热碳排放因子(kg/MJ)'] < 0.2].copy()"
|
|||
|
|
],
|
|||
|
|
"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": 30,
|
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"outputs": [],
|
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"source": [
|
|||
|
|
"import seaborn as sns"
|
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],
|
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"metadata": {
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"collapsed": false,
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},
|
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{
|
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"cell_type": "code",
|
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|
"execution_count": 31,
|
|||
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|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from scipy.stats import norm"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"collapsed": false,
|
|||
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}
|
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|
},
|
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{
|
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"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
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|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "Empty DataFrame\nColumns: [电厂名称, 机组编号, 铭牌容量 (MW), 投产时间, 机组类型, 参数分类, 所处地区, 冷凝器型式, 时间, 发电量(万kWh), 供电量(万kWh), 标煤量（t）, 发电用标煤量（t）, 供热用标煤量（t）, 利用小时(h), 平均负荷(MW), 出力系数(%), 工业供热量, 工业热电比(%), 采暖供热量, 采暖热电比(%), 总热电比(%), 总供热量, 供热煤耗, 给水泵汽轮机总耗热量, 供电煤耗gce/(kWh), 综合厂用电率(%), 发电厂用电率(%), 供热厂用电率(%), 非生产厂用电率(%), 最新THA工况二类修正后汽机热耗率kJ/KWh, 点火用油(kg/万kWh), 助燃用油(kg/万kWh), 主蒸汽压力(MPa), 主蒸汽温度(℃), 再热蒸汽温度(℃), 高加投入率(%), 给水温度(℃), 真空严密性V(Pa/min), 真空度(%), 凝汽器端差(℃), 凝结水过冷度(℃), 发电补给水率(%), 发电综合耗水率, 排烟温度(℃), 飞灰含碳量(%), 空预器漏风率(%), 过热器减温水量(t/h), 再热器减温水量(t/h), 入厂煤低位热值(kJ/kg), 入炉煤低位热值(kJ/kg), 燃煤挥发份Var(%), 燃煤灰份Aar(%), 燃煤低位热值Qar,net(kJ/kg), 燃煤硫份Sar(%), 锅炉专业主要辅机耗电率 送风机耗电率(%), 锅炉专业主要辅机耗电率 引风机耗电率(%), 锅炉专业主要辅机耗电率 一次风机耗电率(%), 锅炉专业主要辅机耗电率 炉水泵耗电率(%), 锅炉专业主要辅机耗电率 给煤机耗电率(%), 锅炉专业主要辅机耗电率 磨煤机耗电率(%), 锅炉专业主要辅机耗电率 电除尘器耗电率(%), 锅炉专业主要辅机耗电率 除灰系统耗电率(%), 汽机专业主要辅机耗电率 凝结水泵耗电率(%), 汽机专业主要辅机耗电率 前置泵耗电率(%), 汽机专业主要辅机耗电率 电动给水泵耗电率(%), 汽机专业主要辅机耗电率 循环水泵耗电率(%), 汽机专业主要辅机耗电率 空冷风机耗电率(%), 汽机专业主要辅机耗电率 热网循环水泵耗电率(%), 环保专业耗电率 脱硫系统耗电率(%), 环保专业耗电率 脱销系统耗电率(%), 输煤专业耗电率输煤系统耗电率(%), 化学系统耗电率 (%), 企业编码, 简称, 制造厂家, 型号, 型式, 最大连续出力B-MCR(t/h), 设计效率(%), 工质流动方式, 额定主蒸汽压力 (MPa), 额再热蒸汽温度 (℃), 额定再热蒸汽压力 (MPa), 额再热蒸汽温度 (℃), 点火方式, 燃烧方式, 设计燃煤种类, 设计燃煤灰份（收到基）(%), 设计燃煤挥发份）（收到基）(%), 设计燃煤低位热值（收到基）（kJ/kg）, 排渣方式, 除灰方式, 空预器制造厂家, 空预器型号, 空预器型式, 空预器设计漏风率(%), 磨煤机制造厂家, 磨煤机型号, 磨煤机型式, ...]\nIndex: []\n\n[0 rows x 134 columns]",
|
|||
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"`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
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}
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],
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"source": [
|
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"sns.distplot(data['供热碳排放因子(kg/MJ)'], fit=norm)"
|
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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": 34,
|
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"outputs": [],
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"source": [
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|
"data[['电厂名称', '机组编号', '铭牌容量 (MW)', '机组类型', '参数分类', '冷凝器型式', '入炉煤低位热值(kJ/kg)', '燃煤挥发份Var(%)', '燃煤灰份Aar(%)', '煤种', '所处地区', 'longitude', 'latitude', 'altitude','发电碳排放因子(kg/kWh)', '供热碳排放因子(kg/MJ)']].to_csv('./train_data.csv', index=False, encoding='utf-8-sig')"
|
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],
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