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{
"cells": [
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"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",
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"execution_count": 10,
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"metadata": {},
"outputs": [],
"source": [
"# 加载数据\n",
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"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",
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"#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",
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"execution_count": 11,
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"metadata": {},
"outputs": [],
"source": [
"# 加载数据\n",
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"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",
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"#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",
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"execution_count": 12,
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"metadata": {},
"outputs": [],
"source": [
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"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",
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"#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",
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"execution_count": 13,
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"metadata": {},
"outputs": [],
"source": [
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"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",
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"#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",
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"execution_count": 14,
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"metadata": {},
"outputs": [],
"source": [
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"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",
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"#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",
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"execution_count": 50,
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"metadata": {},
"outputs": [
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"true_2"
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"f_low"
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]
},
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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{
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"metadata": {},
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],
"source": [
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"pre_data=f_low+f_high\n",
"pre_data\n",
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"true=true_low+true_high\n",
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"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)"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
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"name": "stdout",
"output_type": "stream",
"text": [
"[[4.6702959]\n",
" [4.6353927]\n",
" [4.6446364]\n",
" ...\n",
" [0. ]\n",
" [0. ]\n",
" [0. ]]\n"
]
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}
],
"source": [
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"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)"
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]
},
{
"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
"outputs": [
{
"data": {
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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",
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"plt.plot(updated_pre, label='pre')\n",
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"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",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"mean_squared_error: 0.0018723911403168718\n",
"mean_absolute_error: 0.015867955050384196\n",
"rmse: 0.04488873526226243\n",
"r2 score: 0.9994400158090125\n"
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]
}
],
"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",
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"print('mean_squared_error:', mean_squared_error(updated_pre, true)) # mse)\n",
"print(\"mean_absolute_error:\", mean_absolute_error(updated_pre, true)) # mae\n",
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"print(\"rmse:\", sqrt(mean_squared_error(pre_data, true)))\n",
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"print(\"r2 score:\", r2_score(updated_pre, true))#"
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]
},
{
"cell_type": "code",
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"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,
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],
"source": [
"pre_data3"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
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" 3.26446676e+00, 3.35959959e+00, 3.45073366e+00, 3.53786659e+00,\n",
" 3.60366631e+00, 3.66599941e+00, 3.75313353e+00, 3.82739925e+00,\n",
" 3.88766718e+00, 3.95066643e+00, 4.00006676e+00, 4.04713345e+00,\n",
" 4.10600042e+00, 4.18293333e+00, 4.21853399e+00, 4.25460005e+00,\n",
" 4.23586655e+00, 4.33613300e+00, 4.38013363e+00, 4.21306658e+00,\n",
" 4.37960052e+00, 4.39233303e+00, 4.51066637e+00, 4.54153299e+00,\n",
" 4.62066650e+00, 4.63080025e+00, 4.67740011e+00, 4.64033365e+00,\n",
" 4.65366697e+00, 4.68333292e+00, 4.72733355e+00, 4.75113344e+00,\n",
" 4.75366688e+00, 4.70033312e+00, 4.73833370e+00, 4.76153374e+00,\n",
" 4.72873306e+00, 4.71820068e+00, 4.72226620e+00, 4.73426676e+00,\n",
" 4.74899960e+00, 4.73766708e+00, 4.69826698e+00, 4.72093391e+00,\n",
" 4.67413330e+00, 4.65986633e+00, 4.65800047e+00, 4.63566637e+00,\n",
" 4.60559940e+00, 4.53426600e+00, 4.50513315e+00, 4.49013329e+00,\n",
" 4.41386700e+00, 4.45499992e+00, 4.37726641e+00, 4.41346645e+00,\n",
" 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",
" 3.46993303e+00, 3.41440010e+00, 3.34759975e+00, 3.23346663e+00,\n",
" 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": [
{
"data": {
"image/png": "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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,
上传文件至 ''
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
上传文件至 ''
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"(10415, 1)\n"
上传文件至 ''
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]
}
],
"source": [
"# 使用MinMaxScaler进行归一化\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
上传文件至 ''
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"pre = scaler.fit_transform(updated_pre)\n",
上传文件至 ''
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"print(pre.shape)"
]
},
{
"cell_type": "code",
上传文件至 ''
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"execution_count": 37,
上传文件至 ''
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
上传文件至 ''
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"(10415, 1)\n"
上传文件至 ''
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]
}
],
"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",
上传文件至 ''
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"execution_count": 38,
上传文件至 ''
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
上传文件至 ''
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"mean_squared_error: 0.04675641413227651\n",
"mean_absolute_error: 0.0798491015148862\n",
"rmse: 0.21689357303163628\n",
"r2 score: 0.9912435196234671\n"
上传文件至 ''
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]
}
],
"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",
上传文件至 ''
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"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]))#"
上传文件至 ''
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}