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