{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tensorflow.keras import Sequential\n",
    "\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\单多步可视化\\icial模型\\t+1\\xin9999低频_forecast(T+1).csv\"#数据所在路径\n",
    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
    "f_low= pd.DataFrame(pd.read_csv(path1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "path2 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\单多步可视化\\icial模型\\t+1\\xin99939高频re_forecast(t+1).csv\"#数据所在路径\n",
    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
    "f_high= pd.DataFrame(pd.read_csv(path2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "path3= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\单多步可视化\\icial模型\\t+1\\xin9999低频_test(T+1).csv\"#数据所在路径\n",
    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
    "true_low= pd.DataFrame(pd.read_csv(path3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "path4= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\单多步可视化\\icial模型\\t+1\\xin99939高频re_test(t+1).csv\"#数据所在路径\n",
    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
    "true_high= pd.DataFrame(pd.read_csv(path4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "path5= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\单多步可视化\\icial模型\\t+1\\test.csv\"#数据所在路径\n",
    "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
    "true_2= pd.DataFrame(pd.read_csv(path5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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    "f_low"
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  {
   "cell_type": "code",
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     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "pre_data=f_low+f_high\n",
    "pre_data\n",
    "true=true_low+true_high\n",
    "true\n",
    "# df1 = pd.DataFrame(pre_data, columns=['column_name'])\n",
    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
    "# df1.to_csv('(t+3)经过ICEEMDAN分解预测的预测集.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.6702959]\n",
      " [4.6353927]\n",
      " [4.6446364]\n",
      " ...\n",
      " [0.       ]\n",
      " [0.       ]\n",
      " [0.       ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def update_pre_based_on_true(true, pre):\n",
    "    # 确保 true 和 pre 是 NumPy 数组\n",
    "    true = np.array(true)\n",
    "    pre = np.array(pre)\n",
    "    \n",
    "    # 使用布尔索引将 pre 中对应位置的值设为0\n",
    "    pre[true == 0] = 0\n",
    "    \n",
    "    return pre\n",
    "\n",
    "\n",
    "\n",
    "updated_pre = update_pre_based_on_true(true_2, pre_data)\n",
    "print(updated_pre)\n",
    "df1 = pd.DataFrame(updated_pre, columns=['column_name'])\n",
    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
    "df1.to_csv('(t+1)经过ICEEMDAN分解预测的预测集.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(true_2, label='true')\n",
    "plt.plot(updated_pre, label='pre')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找论文  引用  峰值达不到 偏低 情况 通过分解之后 提高了 加在分析 结论\n",
    "两个数据集篇幅一样  附录 图 未来数据引入 文章对比   3个亮点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error: 0.0018723911403168718\n",
      "mean_absolute_error: 0.015867955050384196\n",
      "rmse: 0.04488873526226243\n",
      "r2 score: 0.9994400158090125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "print('mean_squared_error:', mean_squared_error(updated_pre, true))  # mse)\n",
    "print(\"mean_absolute_error:\", mean_absolute_error(updated_pre, true))  # mae\n",
    "print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
    "print(\"r2 score:\", r2_score(updated_pre, true))#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "true3=true_2[150:400]\n",
    "pre_data3=updated_pre[150:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 假设true_2和updated_pre是你的NumPy数组\n",
    "true3 = true_2[150:400]\n",
    "pre_data3 = updated_pre[150:400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       4.34786653e+00, 4.34599972e+00, 4.29546642e+00, 4.27073288e+00,\n",
       "       4.22719955e+00, 4.16773415e+00, 4.10826588e+00, 4.07379961e+00,\n",
       "       4.01966667e+00, 3.95240021e+00, 3.91913366e+00, 3.82226634e+00,\n",
       "       3.74139977e+00, 3.67379951e+00, 3.63913369e+00, 3.54620004e+00,\n",
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       "       3.16666675e+00, 3.09766650e+00, 3.01139998e+00, 2.92106676e+00,\n",
       "       2.82239986e+00, 2.73320007e+00, 2.61460018e+00, 2.51999998e+00,\n",
       "       2.42013359e+00, 2.31419992e+00, 2.20986652e+00, 2.09686661e+00,\n",
       "       1.99059999e+00, 1.88639998e+00, 1.77793360e+00, 1.64793324e+00,\n",
       "       1.54139996e+00, 1.42546666e+00, 1.30626667e+00, 1.19073319e+00,\n",
       "       1.07273352e+00, 9.58733380e-01, 8.42133343e-01, 7.15199947e-01,\n",
       "       5.81333339e-01, 4.45266664e-01, 3.14866692e-01, 2.03333318e-01,\n",
       "       1.25466660e-01, 8.83999990e-02, 6.87333350e-02, 4.97333260e-02,\n",
       "       3.25333250e-02, 1.56666660e-02, 2.40000000e-03, 1.33333000e-04,\n",
       "       6.67000000e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设df是你的DataFrame\n",
    "true31 = true3['column_name'].to_numpy()\n",
    "true31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(true31, label='true')\n",
    "plt.plot(pre_data3, label='pre')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(updated_pre[150:400], columns=['column_name'])\n",
    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
    "df1.to_csv('1天的经过ICEEMDAN分解预测的预测集1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(true[150:400], columns=['column_name'])\n",
    "# 指定文件路径和文件名，保存DataFrame到CSV文件中\n",
    "df2.to_csv('1天的经过ICEEMDAN分解预测的真实集.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10415, 1)\n"
     ]
    }
   ],
   "source": [
    "# 使用MinMaxScaler进行归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "pre = scaler.fit_transform(updated_pre)\n",
    "print(pre.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10415, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "true_data = scaler.fit_transform(true)\n",
    "print(true_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error: 0.04675641413227651\n",
      "mean_absolute_error: 0.0798491015148862\n",
      "rmse: 0.21689357303163628\n",
      "r2 score: 0.9912435196234671\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error  # 评价指标\n",
    "# 使用sklearn调用衡量线性回归的MSE 、 RMSE、 MAE、r2\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "print('mean_squared_error:', mean_squared_error(updated_pre, true))  # mse)\n",
    "print(\"mean_absolute_error:\", mean_absolute_error(updated_pre, true))  # mae\n",
    "print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
    "print(\"r2 score:\", r2_score(pre[900:2100], true_data[900:2100]))#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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