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2024-08-12 07:42:43 +08:00
{
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"C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
" from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
"C:\\Users\\asus\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
" from pandas.core import (\n"
]
}
],
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"data=pd.read_csv(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\\对比模型\\data3.csv')"
]
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{
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"104255 2019-3-31 23:55 29021.0 1.000000 0.0 13.134816 34.500034 \n",
"\n",
" GHI DHI Wind_dir Rainfall RGT RDT Power \n",
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"source": [
"data2 = data.drop(\"time\", axis=1)"
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" AE_Power Current PR Temp Humidity GHI DHI \\\n",
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"data": {
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"array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,\n",
" <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
" dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+sAAAKTCAYAAABhBplRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xT5f7A8U920s0olEIZAgoIKLLEiYriuup1LxT3AL2IXhHF+VNxXcWB4t64N+BAFERBQVCQVfbshLZJ2zT7/P54krTpTGda+L5fr77SnPOcc54kJyfn+0ydpmkaQgghhBBCCCGEaDX0sc6AEEIIIYQQQgghIkmwLoQQQgghhBBCtDISrAshhBBCCCGEEK2MBOtCCCGEEEIIIUQrI8G6EEIIIYQQQgjRykiwLoQQQgghhBBCtDISrAshhBBCCCGEEK2MMdYZiJVAIEBWVhaJiYnodLpYZ0cIIYQQQgghxH5O0zSKi4tJT09Hr6+97vyADdazsrLIyMiIdTaEEEIIIYQQQhxgdu3aRbdu3WpNc8AG64mJiYB6k5KSkmKcGyGEEEIIIYQ4sGmahtevUeL2Uer2UeL2UuryU+IJPvf4cLp9lLr8FAf/L3H7KHH7KXH5CGga9/9rAAPSk2P9UmrkcDjIyMgIx6O1OWCD9VDT96SkJAnWhRBCCCGEEKIeNE3DUVYeQBe7fZS4fDg9PopdKrgu9fgpdvko85SvL/X4KHX7cXrUc5cvgNcXwO0P4PUH0LTG5UtnjmsT8V00XbEP2GBdCCGEEEIIIQ4EgYCG0+un1K0C6ZKIwDlUi+0PBtjlgXZondsXwOML4PYFKPOUpws0MrCujc1kIMFqJNFqJMGi/uItRhKDj/EWtS70F282YjToOLhz3TXWbYUE60IIIYQQQgjRynj9AUpCgbW7vMba6fGHm4lHBNnu8lprVYvtpdQdTOvxNbrGuiZmo554s4F4S2RQrR4jl8dVDLaD21hNBsxGPWajHpNBh8VoIN5swGiQicskWBdCCCGEEEKIRvL5Azi9/ogAu9Ttw+1VNdIev2oSbnd6KfH4KCjxUOoJ9bf24vT4cQZrrUuCtdlNzaDXEW82kGg1BYNngwqqzaEA20BcKLA2G8KBt9Wkx2wwYDHpsRpVjXe82UCSzYTVZGjyfApFgnUhhBBCCCHEAckf0Chx+Sh2q1rojbnFbMwtJtlmomOCBXuZl5Jg0/FilzfcjLw4XKvtC27vw9MEwbXVqKOdVU9Hmw5QQbDZqCfObMBmUgG0zWwgLvgXXmZSz63hdWp5vMWIzaQn3mIizqSC7SadttrvxeX3Nt3+9hMmkwmDofGFGBKsCyGEEEIIIVo9TdPwBTS8ftV/2uMLRATNdqc32FzcH1GzHQqoQ89DA50Vu3yUef1Nnk+TQaeafQf7UVtMBiwGPRaTCrrbx5uJMxtpF2ciyWZStdpmAyk6J5ZAGQa9Dp0O9KjH6INrf/CvwlMn+AB78E+0nJSUFNLS0hpVOCLBuhBCCCGEEKLJaZqGyxugqMxT3nc6+Fe533VpsAl4idtHmcePxx/A7fXj9gUocnpUU3G3F5e36ZuGg6q9TrAYKSj1hJcd1bsDyTZTcIAzU3igs0RrMBC3GEmyhvpfq3U2s6FBzcKzs7MpKvLQqUsX4uLimrb2W7QoTdNwOp3k5eUB0KVLlwbvS4J1IYQQQgghDmCapuH2BYKBs59it+o/7fL6w/2tnR4f9jIvZcGA2uEKBd2hGmt/uNbaFQyym6JZeG30Oog3q+A5zmIkxWYiwRrZ1zoUXMeZy0cMTwwH2qZg0G3AYoxdv2u/309RURGdOnWiQ4cOMcuHaDo2mw2AvLw8OnXq1OAm8RKsCyGEEEII0cb4A1p4iq3KA5oVu8qn5woF0KHabLevvMbaXqb6YDtczVdjDWDU68IBdKLVRJzFUB5Qm8un4QpN1WUzGbAYVbNxs0FPSpw5ItAOLd9fRgv3elWf77i4uBjnRDSl0Ofp9XolWBdCCCGEEKK1CtVeO8q8OIJTalVsCl5SoTl4iau8xtpZYU5rp0eNJh4KvJuDzWQIjwpuNRlUf2ujHqvJQLs4U3jwsiSrKWJarniLIRx428zBYNtowGpSzculWXfd5D3avzTF5ynBuhBCCCGEENXw+QPhZuGlwT7T+cVu/tpZRF6xm0PTk8I12CUuFVC7fYFw7XWpx4fT7cfh8lLo9OIPNP1E1yaDrsr81hX7VsebVf/qcH9qoyGixjo+OHVXaNAzg14CRiFai3oF69OnT+fzzz9nw4YN2Gw2jjrqKB5//HEOOeSQcJrRo0ezaNGiiO1uuOEGZs2aFX6+c+dObrrpJn7++WcSEhK48sormT59OkZjeXYWLlzI5MmTWbt2LRkZGUybNo3x48dH7HfmzJk8+eST5OTkcNhhh/H8888zYsSI+rwkIYQQQgixHwmNGF7q9gWbefvJL3EHm4p7g1Nx+cMDm4VqtH/OzA/vo2OCmWJX3fNcf/HXnnrnz6DXqT7UJjWfdagWOzRAWahJeHxw2q1Q7XVomq5Ea3kwrua/ljmuhdhf1StYX7RoERMmTGD48OH4fD7uvvtuTjnlFNatW0d8fHw43XXXXcdDDz0Ufl6x/4Xf7+eMM84gLS2NJUuWkJ2dzRVXXIHJZOLRRx8FYNu2bZxxxhnceOONvP/++yxYsIBrr72WLl26MHbsWAA++ugjJk+ezKxZsxg5ciQzZsxg7NixZGZm0qlTp0a9KUIIIYQQovn5/AE8wWm4Sj1+3F5/+HmoP3Wxy0ehU40mHp7r2uMLjy4e6q/t9PhweQO4fX4aW4G9t8QT8Tw0Unii1YjPr7GnqAyAsw9PD0/RlWhRg5jZzAbMFabpSrCo5uIdEywq6DYZ0EvttRAiCjpN0xp8OcvPz6dTp04sWrSI4447DlA164cffjgzZsyodptvv/2WM888k6ysLDp37gzArFmzmDJlCvn5+ZjNZqZMmcLcuXNZs2ZNeLuLL76YoqIivvvuOwBGjhzJ8OHDeeGFFwAIBAJkZGRwyy23cNddd9WZd4fDQXJyMna7naSkpIa+BUIIIYQQ+7XQ9FuhKbVUf2s1P/X3a3PZU1TG8B7tCGhQ4vYGp9hStdUur798Tmy/htNTPmp4UwTVdYk3q9rr9nFmkuNMai7rYNPveEt5gJ1gNbIlr4QSt48RvdpzcOfEcLPyBKsR034ykJlonVwuF9u2baNXr15YrdZYZ6dBli5dyjHHHMOpp57K3Llzw8u3b99Or169atzmyCOPrHW/b731FldddRWg+oCnp6dz8skn8/jjj7f6CtqaPtf6xKGN6rNut9sBaN++fcTy999/n/fee4+0tDT+9a9/ce+994Zr15cuXcqgQYPCgTrA2LFjuemmm1i7di1Dhgxh6dKljBkzJmKfY8eOZdKkSQB4PB5WrFjB1KlTw+v1ej1jxoxh6dKl1ebV7XbjdrvDzx0OR8NfuBBCCCFEjHl8AVw+P54KfaQr1kA7PT78AQ1vQMPvD+ALaOq5P1BldHBPhT+np3wO7FDQXVdQ/cvG/NoTRMFs1AcHJVP9qROsahCzOIuRDvHlAXaS1VRhWi5DRH9tq6l8YDOLSS9NxIVoIa+//jq33HILr7/+OllZWaSnp0es//HHHzn00EMjlkU7TV1SUhKZmZkEAgFWrVrFVVddRVZWFt9//32T5b+hNE3D7/dHdOduSg3eayAQYNKkSRx99NEMHDgwvPzSSy+lR48epKens3r1aqZMmUJmZiaff/45ADk5ORGBOhB+npOTU2sah8NBWVkZhYWF+P3+atNs2LCh2vxOnz6dBx98sKEvVwghhBAiahUbLvoCWripttsXYNveUnYXOumQYCHRaqTU7WPOqmyKyjwc06cjZqOeIqc3POq3I9jXOjSQWbFLNf32+Jt3Duvq2EyGcH9pm9nA2ixV+TG4WzKHpieH14VGB7eaVJNws1H9WU3lfbOtZn14ncmgl9prISrQNI0yb/OM+F8Xm8lQr5HMS0pK+Oijj/jzzz/Jycnhrbfe4u67745I06FDB9LS0hqUH51OF94
"text/plain": [
"<Figure size 1200x800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data2.plot(legend=True, subplots=True, figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 104256 entries, 0 to 104255\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 AE_Power 104205 non-null float64\n",
" 1 Current 104205 non-null float64\n",
" 2 PR 104205 non-null float64\n",
" 3 Temp 104256 non-null float64\n",
" 4 Humidity 104256 non-null float64\n",
" 5 GHI 104256 non-null float64\n",
" 6 DHI 104256 non-null float64\n",
" 7 Wind_dir 104256 non-null float64\n",
" 8 Rainfall 104256 non-null float64\n",
" 9 RGT 103982 non-null float64\n",
" 10 RDT 103982 non-null float64\n",
" 11 Power 104205 non-null float64\n",
"dtypes: float64(12)\n",
"memory usage: 9.5 MB\n"
]
}
],
"source": [
"data2.info()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data3 = data2.drop(\"AE_Power\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"Current\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"PR\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"Wind_dir\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"RGT\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"RDT\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"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>Temp</th>\n",
" <th>Humidity</th>\n",
" <th>GHI</th>\n",
" <th>DHI</th>\n",
" <th>Rainfall</th>\n",
" <th>Power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19.779453</td>\n",
" <td>40.025826</td>\n",
" <td>3.232706</td>\n",
" <td>1.690531</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19.714937</td>\n",
" <td>39.605961</td>\n",
" <td>3.194991</td>\n",
" <td>1.576346</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19.549330</td>\n",
" <td>39.608631</td>\n",
" <td>3.070866</td>\n",
" <td>1.576157</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19.405870</td>\n",
" <td>39.680702</td>\n",
" <td>3.038623</td>\n",
" <td>1.482489</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19.387363</td>\n",
" <td>39.319881</td>\n",
" <td>2.656474</td>\n",
" <td>1.134153</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104251</th>\n",
" <td>13.303740</td>\n",
" <td>34.212711</td>\n",
" <td>1.210789</td>\n",
" <td>0.787026</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104252</th>\n",
" <td>13.120920</td>\n",
" <td>34.394939</td>\n",
" <td>2.142980</td>\n",
" <td>1.582670</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104253</th>\n",
" <td>12.879215</td>\n",
" <td>35.167400</td>\n",
" <td>1.926214</td>\n",
" <td>1.545889</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104254</th>\n",
" <td>12.915867</td>\n",
" <td>35.359989</td>\n",
" <td>1.317695</td>\n",
" <td>0.851529</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104255</th>\n",
" <td>13.134816</td>\n",
" <td>34.500034</td>\n",
" <td>1.043269</td>\n",
" <td>0.597816</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>104256 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Temp Humidity GHI DHI Rainfall Power\n",
"0 19.779453 40.025826 3.232706 1.690531 0.0 0.0\n",
"1 19.714937 39.605961 3.194991 1.576346 0.0 0.0\n",
"2 19.549330 39.608631 3.070866 1.576157 0.0 0.0\n",
"3 19.405870 39.680702 3.038623 1.482489 0.0 0.0\n",
"4 19.387363 39.319881 2.656474 1.134153 0.0 0.0\n",
"... ... ... ... ... ... ...\n",
"104251 13.303740 34.212711 1.210789 0.787026 0.0 0.0\n",
"104252 13.120920 34.394939 2.142980 1.582670 0.0 0.0\n",
"104253 12.879215 35.167400 1.926214 1.545889 0.0 0.0\n",
"104254 12.915867 35.359989 1.317695 0.851529 0.0 0.0\n",
"104255 13.134816 34.500034 1.043269 0.597816 0.0 0.0\n",
"\n",
"[104256 rows x 6 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
" dtype=object)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x800 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data3.plot(legend=True, subplots=True, figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#各个相关性分析\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.rcParams['font.sans-serif']=['SimHei']\n",
"plt.rcParams[\"font.sans-serif\"] = [\"SimSun\", \"Arial Unicode MS\"]\n",
"plt.rcParams[\"axes.unicode_minus\"] = False\n",
"import seaborn as sns\n",
"sns.set(font_scale=1.5)\n",
"sns.heatmap(data3.corr(),annot=True,cmap='RdYlGn',linewidths=0.2, fmt='.2f')\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(10,8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"Humidity\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"data3 = data3.drop(\"Rainfall\",axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataFrame saved to 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\\对比模型\\data7.csv\n"
]
}
],
"source": [
"data4 = pd.DataFrame(data3)\n",
"\n",
"# 将 data3 保存为 Excel 文件\n",
"csv_file_path = '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\\对比模型\\data7.csv' # 定义 Excel 文件路径和文件名\n",
"\n",
"data4.to_csv(csv_file_path, index=False) # 将 DataFrame 保存为 Excel 文件，不包含索引\n",
"\n",
"print(f\"DataFrame saved to {csv_file_path}\")"
]
},
{
"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
}