{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:42.420799Z",
     "end_time": "2025-02-09T19:14:42.448185Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:42.979075Z",
     "end_time": "2025-02-09T19:14:43.042942Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  光伏用户编号  综合倍率             时间      p1      p2      p3      p4   p5      p6  \\\n0     f1    80  2022-1-3 0:00  0.0000  0.0000  0.0000  0.0000  0.0  0.0000   \n1     f1    80  2022-1-4 0:00 -0.0001  0.0000 -0.0001  0.0000  0.0 -0.0001   \n2     f1    80  2022-1-5 0:00 -0.0003 -0.0001  0.0000  0.0000  0.0 -0.0001   \n3     f1    80  2022-1-6 0:00 -0.0003  0.0000 -0.0003 -0.0001  0.0  0.0000   \n4     f1    80  2022-1-7 0:00  0.0000 -0.0001 -0.0001  0.0000  0.0 -0.0001   \n\n       p7  ...     p87     p88     p89     p90     p91     p92     p93  \\\n0 -0.0001  ... -0.0031 -0.0032 -0.0032 -0.0033 -0.0001 -0.0001 -0.0003   \n1 -0.0001  ... -0.0032 -0.0032 -0.0032 -0.0031 -0.0001  0.0000 -0.0001   \n2 -0.0001  ... -0.0016 -0.0016 -0.0016 -0.0017  0.0000  0.0000  0.0000   \n3  0.0000  ... -0.0016 -0.0016 -0.0016 -0.0016  0.0000  0.0000  0.0000   \n4 -0.0003  ...  0.0000 -0.0001  0.0000  0.0000  0.0000 -0.0001  0.0000   \n\n      p94     p95     p96  \n0 -0.0003 -0.0003 -0.0001  \n1 -0.0001 -0.0001 -0.0001  \n2  0.0000  0.0000 -0.0001  \n3 -0.0001  0.0000  0.0000  \n4  0.0000  0.0000  0.0000  \n\n[5 rows x 99 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>光伏用户编号</th>\n      <th>综合倍率</th>\n      <th>时间</th>\n      <th>p1</th>\n      <th>p2</th>\n      <th>p3</th>\n      <th>p4</th>\n      <th>p5</th>\n      <th>p6</th>\n      <th>p7</th>\n      <th>...</th>\n      <th>p87</th>\n      <th>p88</th>\n      <th>p89</th>\n      <th>p90</th>\n      <th>p91</th>\n      <th>p92</th>\n      <th>p93</th>\n      <th>p94</th>\n      <th>p95</th>\n      <th>p96</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>f1</td>\n      <td>80</td>\n      <td>2022-1-3 0:00</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>...</td>\n      <td>-0.0031</td>\n      <td>-0.0032</td>\n      <td>-0.0032</td>\n      <td>-0.0033</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0003</td>\n      <td>-0.0001</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>f1</td>\n      <td>80</td>\n      <td>2022-1-4 0:00</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>...</td>\n      <td>-0.0032</td>\n      <td>-0.0032</td>\n      <td>-0.0032</td>\n      <td>-0.0031</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>f1</td>\n      <td>80</td>\n      <td>2022-1-5 0:00</td>\n      <td>-0.0003</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>...</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>-0.0017</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>f1</td>\n      <td>80</td>\n      <td>2022-1-6 0:00</td>\n      <td>-0.0003</td>\n      <td>0.0000</td>\n      <td>-0.0003</td>\n      <td>-0.0001</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>...</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>f1</td>\n      <td>80</td>\n      <td>2022-1-7 0:00</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>-0.0001</td>\n      <td>-0.0003</td>\n      <td>...</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 99 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/pv_original.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:44.457393Z",
     "end_time": "2025-02-09T19:14:44.483344Z"
    }
   },
   "outputs": [],
   "source": [
    "use_data = data[data['光伏用户编号']=='f1'].sort_values(by='时间').drop(columns=data.columns[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:46.754912Z",
     "end_time": "2025-02-09T19:14:46.763512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "        p1      p2      p3      p4      p5      p6      p7      p8      p9  \\\n7   0.0000  0.0000  0.0000 -0.0001 -0.0001  0.0000  0.0000  0.0000 -0.0003   \n8  -0.0001 -0.0001  0.0000  0.0000 -0.0001  0.0000 -0.0003  0.0000 -0.0001   \n9  -0.0001 -0.0001  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000 -0.0001   \n10  0.0000  0.0000  0.0000 -0.0001  0.0000 -0.0001 -0.0001 -0.0001  0.0000   \n11  0.0000  0.0000 -0.0001  0.0000 -0.0001 -0.0001 -0.0001  0.0000  0.0000   \n\n       p10  ...     p87     p88     p89     p90     p91     p92     p93  \\\n7   0.0000  ... -0.0017 -0.0017 -0.0017 -0.0017 -0.0001  0.0000 -0.0001   \n8   0.0000  ... -0.0032 -0.0031 -0.0032 -0.0032 -0.0017 -0.0017 -0.0001   \n9  -0.0001  ... -0.0023 -0.0016 -0.0016 -0.0019  0.0000 -0.0001  0.0000   \n10 -0.0001  ... -0.0032 -0.0033 -0.0032 -0.0033 -0.0017 -0.0016 -0.0017   \n11  0.0000  ... -0.0016 -0.0017 -0.0017 -0.0016  0.0000  0.0000  0.0000   \n\n       p94     p95     p96  \n7   0.0000  0.0000  0.0000  \n8   0.0000 -0.0001  0.0000  \n9   0.0000 -0.0003  0.0000  \n10 -0.0019 -0.0015 -0.0017  \n11 -0.0001  0.0000 -0.0001  \n\n[5 rows x 96 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>p1</th>\n      <th>p2</th>\n      <th>p3</th>\n      <th>p4</th>\n      <th>p5</th>\n      <th>p6</th>\n      <th>p7</th>\n      <th>p8</th>\n      <th>p9</th>\n      <th>p10</th>\n      <th>...</th>\n      <th>p87</th>\n      <th>p88</th>\n      <th>p89</th>\n      <th>p90</th>\n      <th>p91</th>\n      <th>p92</th>\n      <th>p93</th>\n      <th>p94</th>\n      <th>p95</th>\n      <th>p96</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7</th>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0003</td>\n      <td>0.0000</td>\n      <td>...</td>\n      <td>-0.0017</td>\n      <td>-0.0017</td>\n      <td>-0.0017</td>\n      <td>-0.0017</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0003</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>...</td>\n      <td>-0.0032</td>\n      <td>-0.0031</td>\n      <td>-0.0032</td>\n      <td>-0.0032</td>\n      <td>-0.0017</td>\n      <td>-0.0017</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>...</td>\n      <td>-0.0023</td>\n      <td>-0.0016</td>\n      <td>-0.0016</td>\n      <td>-0.0019</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0003</td>\n      <td>0.0000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>...</td>\n      <td>-0.0032</td>\n      <td>-0.0033</td>\n      <td>-0.0032</td>\n      <td>-0.0033</td>\n      <td>-0.0017</td>\n      <td>-0.0016</td>\n      <td>-0.0017</td>\n      <td>-0.0019</td>\n      <td>-0.0015</td>\n      <td>-0.0017</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>...</td>\n      <td>-0.0016</td>\n      <td>-0.0017</td>\n      <td>-0.0017</td>\n      <td>-0.0016</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n      <td>0.0000</td>\n      <td>-0.0001</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 96 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "         p1      p2      p3      p4      p5      p6      p7      p8      p9  \\\n458 -0.0012 -0.0012 -0.0011 -0.0011 -0.0011 -0.0011 -0.0011 -0.0013 -0.0012   \n459 -0.0012 -0.0011 -0.0012 -0.0012 -0.0011 -0.0012 -0.0012 -0.0011 -0.0011   \n460 -0.0012 -0.0011 -0.0012 -0.0012 -0.0011 -0.0011 -0.0011 -0.0011 -0.0012   \n461 -0.0012 -0.0012 -0.0011 -0.0011 -0.0012 -0.0011 -0.0011 -0.0012 -0.0011   \n462 -0.0011 -0.0011 -0.0011 -0.0011 -0.0011 -0.0012 -0.0011 -0.0012 -0.0011   \n\n        p10  ...     p87     p88     p89     p90     p91     p92     p93  \\\n458 -0.0013  ... -0.0011 -0.0011 -0.0012 -0.0012 -0.0012 -0.0011 -0.0012   \n459 -0.0011  ... -0.0033 -0.0036 -0.0011 -0.0013 -0.0011 -0.0011 -0.0011   \n460 -0.0011  ... -0.0011 -0.0012 -0.0012 -0.0012 -0.0012 -0.0011 -0.0011   \n461 -0.0011  ... -0.0011 -0.0013 -0.0011 -0.0012 -0.0011 -0.0012 -0.0012   \n462 -0.0011  ... -0.0012 -0.0011 -0.0012 -0.0011 -0.0012 -0.0012 -0.0011   \n\n        p94     p95     p96  \n458 -0.0011 -0.0012 -0.0012  \n459 -0.0011 -0.0011 -0.0011  \n460 -0.0011 -0.0011 -0.0011  \n461 -0.0011 -0.0012 -0.0012  \n462 -0.0011 -0.0011 -0.0011  \n\n[5 rows x 96 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>p1</th>\n      <th>p2</th>\n      <th>p3</th>\n      <th>p4</th>\n      <th>p5</th>\n      <th>p6</th>\n      <th>p7</th>\n      <th>p8</th>\n      <th>p9</th>\n      <th>p10</th>\n      <th>...</th>\n      <th>p87</th>\n      <th>p88</th>\n      <th>p89</th>\n      <th>p90</th>\n      <th>p91</th>\n      <th>p92</th>\n      <th>p93</th>\n      <th>p94</th>\n      <th>p95</th>\n      <th>p96</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>458</th>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0013</td>\n      <td>-0.0012</td>\n      <td>-0.0013</td>\n      <td>...</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n    </tr>\n    <tr>\n      <th>459</th>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>...</td>\n      <td>-0.0033</td>\n      <td>-0.0036</td>\n      <td>-0.0011</td>\n      <td>-0.0013</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n    </tr>\n    <tr>\n      <th>460</th>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>...</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n    </tr>\n    <tr>\n      <th>461</th>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>...</td>\n      <td>-0.0011</td>\n      <td>-0.0013</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n    </tr>\n    <tr>\n      <th>462</th>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>...</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0012</td>\n      <td>-0.0012</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n      <td>-0.0011</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 96 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_data[-5:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:48.513486Z",
     "end_time": "2025-02-09T19:14:48.531632Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:57.816980Z",
     "end_time": "2025-02-09T19:14:57.827730Z"
    }
   },
   "outputs": [],
   "source": [
    "# 生成日期范围（484天）\n",
    "dates = pd.date_range(start='2022-01-03', periods=484, freq='D')\n",
    "# 生成每日的96个时间点（15分钟间隔）\n",
    "time_offsets = pd.timedelta_range(start='0min', periods=96, freq='15min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:14:59.274699Z",
     "end_time": "2025-02-09T19:14:59.284894Z"
    }
   },
   "outputs": [],
   "source": [
    "# 创建完整的时间索引\n",
    "datetime_index = dates.repeat(96) + np.tile(time_offsets, len(dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T19:15:02.587749Z",
     "end_time": "2025-02-09T19:15:02.608881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                index    0\n0 2022-01-03 00:00:00  0.0\n1 2022-01-03 01:00:00  0.0\n2 2022-01-03 02:00:00  0.0\n3 2022-01-03 03:00:00  0.0\n4 2022-01-03 04:00:00  0.0",
      "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>index</th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2022-01-03 00:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2022-01-03 01:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2022-01-03 02:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2022-01-03 03:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022-01-03 04:00:00</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 展平数据并创建Series\n",
    "data_series = pd.Series(use_data.values.flatten(), index=datetime_index)\n",
    "\n",
    "# 处理负值\n",
    "data_series = data_series.clip(lower=0)\n",
    "\n",
    "# 按小时重采样求和\n",
    "hourly_data = data_series.resample('h').sum().to_frame().reset_index()\n",
    "\n",
    "# 结果展示\n",
    "hourly_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "                    index    0\n0     2022-01-03 00:00:00  0.0\n1     2022-01-03 01:00:00  0.0\n2     2022-01-03 02:00:00  0.0\n3     2022-01-03 03:00:00  0.0\n4     2022-01-03 04:00:00  0.0\n...                   ...  ...\n11611 2023-05-01 19:00:00  0.0\n11612 2023-05-01 20:00:00  0.0\n11613 2023-05-01 21:00:00  0.0\n11614 2023-05-01 22:00:00  0.0\n11615 2023-05-01 23:00:00  0.0\n\n[11616 rows x 2 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>index</th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2022-01-03 00:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2022-01-03 01:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2022-01-03 02:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2022-01-03 03:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022-01-03 04:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>11611</th>\n      <td>2023-05-01 19:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11612</th>\n      <td>2023-05-01 20:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11613</th>\n      <td>2023-05-01 21:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11614</th>\n      <td>2023-05-01 22:00:00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11615</th>\n      <td>2023-05-01 23:00:00</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>11616 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2025-02-09T19:15:46.762887Z",
     "end_time": "2025-02-09T19:15:46.771869Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_data.columns = ['time', 'power']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x77d18f188fd0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hourly_data.power[:500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_data.to_csv('pv_data_hourly.csv', index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2025-02-09T16:59:07.276741Z",
     "end_time": "2025-02-09T16:59:07.278428Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "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
}