forked from EEBD_AI/wgz_forecast
423 lines
71 KiB
Plaintext
423 lines
71 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": 198,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.412010Z",
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"end_time": "2025-02-09T19:59:53.421172Z"
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}
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},
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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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},
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{
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"cell_type": "code",
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"execution_count": 199,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"原始数据：\n",
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" zone_id year month day h1 h2 h3 h4 h5 h6 \\\n",
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"0 1 2004 1 1 16,853 16,450 16,517 16,873 17,064 17,727 \n",
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"1 1 2004 1 2 14,155 14,038 14,019 14,489 14,920 16,072 \n",
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"2 1 2004 1 3 14,439 14,272 14,109 14,081 14,775 15,491 \n",
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"3 1 2004 1 4 11,273 10,415 9,943 9,859 9,881 10,248 \n",
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"4 1 2004 1 5 10,750 10,321 10,107 10,065 10,419 12,101 \n",
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"\n",
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" ... h15 h16 h17 h18 h19 h20 h21 h22 \\\n",
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"0 ... 13,518 13,138 14,130 16,809 18,150 18,235 17,925 16,904 \n",
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"1 ... 16,127 15,448 15,839 17,727 18,895 18,650 18,443 17,580 \n",
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"2 ... 13,507 13,414 13,826 15,825 16,996 16,394 15,406 14,278 \n",
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"3 ... 14,207 13,614 14,162 16,237 17,430 17,218 16,633 15,238 \n",
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"4 ... 13,845 14,350 15,501 17,307 18,786 19,089 19,192 18,416 \n",
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"\n",
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" h23 h24 \n",
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"0 16,162 14,750 \n",
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"1 16,467 15,258 \n",
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"2 13,315 12,424 \n",
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"3 13,580 11,727 \n",
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"4 17,006 16,018 \n",
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"\n",
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"[5 rows x 28 columns]\n",
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"\n",
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"清理后的数据：\n",
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" zone_id year month day h1 h2 h3 h4 h5 h6 \\\n",
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"0 1 2004 1 1 16.853 16.450 16.517 16.873 17.064 17.727 \n",
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"1 1 2004 1 2 14.155 14.038 14.019 14.489 14.920 16.072 \n",
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"2 1 2004 1 3 14.439 14.272 14.109 14.081 14.775 15.491 \n",
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"3 1 2004 1 4 11.273 10.415 9.943 9.859 9.881 10.248 \n",
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"4 1 2004 1 5 10.750 10.321 10.107 10.065 10.419 12.101 \n",
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"\n",
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" ... h15 h16 h17 h18 h19 h20 h21 h22 \\\n",
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"0 ... 13.518 13.138 14.130 16.809 18.150 18.235 17.925 16.904 \n",
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"1 ... 16.127 15.448 15.839 17.727 18.895 18.650 18.443 17.580 \n",
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"2 ... 13.507 13.414 13.826 15.825 16.996 16.394 15.406 14.278 \n",
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"3 ... 14.207 13.614 14.162 16.237 17.430 17.218 16.633 15.238 \n",
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"4 ... 13.845 14.350 15.501 17.307 18.786 19.089 19.192 18.416 \n",
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"\n",
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" h23 h24 \n",
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"0 16.162 14.750 \n",
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"1 16.467 15.258 \n",
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"2 13.315 12.424 \n",
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"3 13.580 11.727 \n",
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"4 17.006 16.018 \n",
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"\n",
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"[5 rows x 28 columns]\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# 读取原始 CSV 文件\n",
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"data = pd.read_csv('./data/load_original.csv')\n",
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"\n",
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"# 打印原始数据的前几行以检查格式\n",
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"print(\"原始数据：\")\n",
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"print(data.head())\n",
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"\n",
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"# 定义一个函数来清理数值列\n",
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"def clean_numeric_column(column):\n",
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" # 将逗号替换为小数点，并转换为浮点数\n",
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" return column.apply(lambda x: float(str(x).replace(',', '.')) if isinstance(x, str) else x)\n",
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"\n",
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"# 获取所有需要清理的数值列（假设从 'h1' 到 'h24'）\n",
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"numeric_columns = ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7', 'h8', 'h9', 'h10',\n",
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" 'h11', 'h12', 'h13', 'h14', 'h15', 'h16', 'h17', 'h18', 'h19',\n",
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" 'h20', 'h21', 'h22', 'h23', 'h24']\n",
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"\n",
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"# 对每个数值列应用清理函数\n",
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"for col in numeric_columns:\n",
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" data[col] = clean_numeric_column(data[col])\n",
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"\n",
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"# 打印清理后的数据\n",
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"print(\"\\n清理后的数据：\")\n",
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"print(data.head())"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.416168Z",
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"end_time": "2025-02-09T19:59:53.802570Z"
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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": 200,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.802570Z",
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"end_time": "2025-02-09T19:59:53.806323Z"
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}
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},
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"outputs": [],
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"source": [
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"use_data = data[data['zone_id']==1].drop(columns=data.columns[:4])"
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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": 201,
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"outputs": [],
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"source": [
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"user_data_flatten=use_data.values.flatten()"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.807321Z",
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"end_time": "2025-02-09T19:59:53.812420Z"
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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": 202,
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"outputs": [
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{
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"data": {
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"text/plain": "array([16.853, 16.45 , 16.517, ..., nan, nan, nan])"
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},
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"execution_count": 202,
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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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"user_data_flatten"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.813420Z",
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"end_time": "2025-02-09T19:59:53.818353Z"
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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": 203,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"39600\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\liuhao\\AppData\\Local\\Temp\\ipykernel_18300\\3270028511.py:6: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
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" time_index = pd.date_range(start=start_date, periods=len(use_data.values.flatten()), freq='15T')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# 定义起始日期和时间\n",
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"start_date = '2004-01-01 00:00:00'\n",
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"# 使用 pd.date_range 生成时间索引\n",
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"time_index = pd.date_range(start=start_date, periods=len(use_data.values.flatten()), freq='15T')\n",
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"\n",
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"# 打印生成的时间索引\n",
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"print(len(time_index))"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.819353Z",
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"end_time": "2025-02-09T19:59:53.822932Z"
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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": 204,
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"outputs": [
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{
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"data": {
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"text/plain": "DatetimeIndex(['2004-01-01 00:00:00', '2004-01-01 00:15:00',\n '2004-01-01 00:30:00', '2004-01-01 00:45:00',\n '2004-01-01 01:00:00', '2004-01-01 01:15:00',\n '2004-01-01 01:30:00', '2004-01-01 01:45:00',\n '2004-01-01 02:00:00', '2004-01-01 02:15:00',\n ...\n '2005-02-16 09:30:00', '2005-02-16 09:45:00',\n '2005-02-16 10:00:00', '2005-02-16 10:15:00',\n '2005-02-16 10:30:00', '2005-02-16 10:45:00',\n '2005-02-16 11:00:00', '2005-02-16 11:15:00',\n '2005-02-16 11:30:00', '2005-02-16 11:45:00'],\n dtype='datetime64[ns]', length=39600, freq='15min')"
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},
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"execution_count": 204,
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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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"time_index"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.822932Z",
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"end_time": "2025-02-09T19:59:53.827720Z"
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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": 205,
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"outputs": [
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{
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"data": {
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"text/plain": "array([16.853, 16.45 , 16.517, ..., nan, nan, nan])"
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},
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"execution_count": 205,
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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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"use_data.values.flatten()"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.828720Z",
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"end_time": "2025-02-09T19:59:53.832633Z"
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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": 206,
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"outputs": [],
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"source": [
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"# 展平数据并创建Series\n",
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"data_series = pd.Series(use_data.values.flatten(), index=time_index)\n"
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],
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"metadata": {
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|||
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"collapsed": false,
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|||
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.832633Z",
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"end_time": "2025-02-09T19:59:53.880694Z"
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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": 207,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T19:59:53.837441Z",
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"end_time": "2025-02-09T19:59:53.888760Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" index 0\n",
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"0 2004-01-01 00:00:00 66.693\n",
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"1 2004-01-01 01:00:00 72.720\n",
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"2 2004-01-01 02:00:00 72.185\n",
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"3 2004-01-01 03:00:00 56.217\n",
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"4 2004-01-01 04:00:00 67.324\n",
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"... ... ...\n",
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"9895 2005-02-16 07:00:00 0.000\n",
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"9896 2005-02-16 08:00:00 0.000\n",
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"9897 2005-02-16 09:00:00 0.000\n",
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"9898 2005-02-16 10:00:00 0.000\n",
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"9899 2005-02-16 11:00:00 0.000\n",
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"\n",
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"[9900 rows x 2 columns]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\liuhao\\AppData\\Local\\Temp\\ipykernel_18300\\3846816627.py:2: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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" hourly_data = data_series.resample('H').sum().to_frame().reset_index()\n"
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]
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}
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],
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"source": [
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"# 重采样为每小时，并对每小时的数据进行求和\n",
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"hourly_data = data_series.resample('H').sum().to_frame().reset_index()\n",
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"\n",
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"# 打印结果\n",
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"print(hourly_data)"
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]
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|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 208,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T19:59:53.847029Z",
|
|||
|
|
"end_time": "2025-02-09T19:59:53.888760Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"hourly_data.columns = ['time', 'power']"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 209,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T19:59:53.850505Z",
|
|||
|
|
"end_time": "2025-02-09T19:59:53.888760Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 210,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T19:59:53.853687Z",
|
|||
|
|
"end_time": "2025-02-09T19:59:53.949802Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "[<matplotlib.lines.Line2D at 0x2d37c1168d0>]"
|
|||
|
|
},
|
|||
|
|
"execution_count": 210,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|||
|
|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.plot(hourly_data.power[:500])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 211,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T19:59:53.922540Z",
|
|||
|
|
"end_time": "2025-02-09T19:59:53.950868Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"hourly_data.to_csv('data/load_data_hourly.csv', index=False, encoding='utf-8-sig')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 211,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T19:59:53.947223Z",
|
|||
|
|
"end_time": "2025-02-09T19:59:53.950868Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "py39",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.9.21"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|