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ICEEMDAN-Solar_power-forecast/数据预处理.ipynb

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上传文件至 ''
2024-08-12 07:42:43 +08:00
{
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{
"cell_type": "code",
"execution_count": 1,
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"outputs": [
{
"name": "stderr",
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"text": [
"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\\58-Site_DKA-M17_C-Phase.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"text/plain": [
" timestamp Active_Energy_Delivered_Received \\\n",
"0 2010-02-18 14:35:00 0.0 \n",
"1 2010-02-18 14:40:00 0.0 \n",
"2 2010-02-18 14:45:00 0.0 \n",
"3 2010-02-18 14:50:00 0.0 \n",
"4 2010-02-18 14:55:00 0.0 \n",
"... ... ... \n",
"1502295 2024-07-17 12:20:00 83264.0 \n",
"1502296 2024-07-17 12:25:00 83265.0 \n",
"1502297 2024-07-17 12:30:00 83265.0 \n",
"1502298 2024-07-17 12:35:00 83265.0 \n",
"1502299 2024-07-17 12:40:00 83266.0 \n",
"\n",
" Current_Phase_Average Active_Power Performance_Ratio Wind_Speed \\\n",
"0 0.000000 0.000000 NaN 6.793873 \n",
"1 0.000000 0.000000 NaN 6.926013 \n",
"2 0.000000 0.000000 NaN 6.824874 \n",
"3 0.000000 0.000000 NaN 5.291194 \n",
"4 0.000000 0.000000 NaN 6.065388 \n",
"... ... ... ... ... \n",
"1502295 17.695337 4.331866 87.961319 NaN \n",
"1502296 17.795330 4.350333 90.215775 NaN \n",
"1502297 17.962000 4.386533 94.664726 NaN \n",
"1502298 17.877998 4.375267 93.486641 NaN \n",
"1502299 17.829998 4.369600 90.526978 NaN \n",
"\n",
" Weather_Temperature_Celsius Weather_Relative_Humidity \\\n",
"0 35.132046 13.933495 \n",
"1 34.586330 14.363612 \n",
"2 34.628662 13.933328 \n",
"3 35.258572 13.457552 \n",
"4 35.220058 13.886837 \n",
"... ... ... \n",
"1502295 NaN NaN \n",
"1502296 NaN NaN \n",
"1502297 14.208377 33.600403 \n",
"1502298 14.223358 33.683571 \n",
"1502299 14.428312 32.949017 \n",
"\n",
" Global_Horizontal_Radiation Diffuse_Horizontal_Radiation \\\n",
"0 1000.515625 97.682610 \n",
"1 989.110413 102.564949 \n",
"2 977.882629 102.709160 \n",
"3 963.508484 100.324097 \n",
"4 939.744995 105.697617 \n",
"... ... ... \n",
"1502295 NaN NaN \n",
"1502296 NaN NaN \n",
"1502297 823.925476 83.903313 \n",
"1502298 817.790710 76.371666 \n",
"1502299 820.284790 74.797913 \n",
"\n",
" Wind_Direction Weather_Daily_Rainfall Radiation_Global_Tilted \\\n",
"0 126.266418 0.0 NaN \n",
"1 116.272385 0.0 NaN \n",
"2 141.693970 0.0 NaN \n",
"3 130.381912 0.0 NaN \n",
"4 126.441544 0.0 NaN \n",
"... ... ... ... \n",
"1502295 NaN NaN 947.065369 \n",
"1502296 NaN NaN 927.335022 \n",
"1502297 19.253492 0.0 891.106995 \n",
"1502298 19.294001 0.0 900.018799 \n",
"1502299 19.167789 0.0 928.239990 \n",
"\n",
" Radiation_Diffuse_Tilted \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"1502295 144.291245 \n",
"1502296 131.287155 \n",
"1502297 126.447548 \n",
"1502298 123.445114 \n",
"1502299 123.938103 \n",
"\n",
"[1502300 rows x 14 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#只要2018.4.1-2019.4.1一年的数据\n",
"data2=data.iloc[853133:958253, :]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <th>timestamp</th>\n",
" <th>Active_Energy_Delivered_Received</th>\n",
" <th>Current_Phase_Average</th>\n",
" <th>Active_Power</th>\n",
" <th>Performance_Ratio</th>\n",
" <th>Wind_Speed</th>\n",
" <th>Weather_Temperature_Celsius</th>\n",
" <th>Weather_Relative_Humidity</th>\n",
" <th>Global_Horizontal_Radiation</th>\n",
" <th>Diffuse_Horizontal_Radiation</th>\n",
" <th>Wind_Direction</th>\n",
" <th>Weather_Daily_Rainfall</th>\n",
" <th>Radiation_Global_Tilted</th>\n",
" <th>Radiation_Diffuse_Tilted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>853133</th>\n",
" <td>2018-04-01 00:00:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
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" <td>3.232706</td>\n",
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" <td>3.565593</td>\n",
" <td>0.742383</td>\n",
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" <tr>\n",
" <th>853134</th>\n",
" <td>2018-04-01 00:05:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>3.194991</td>\n",
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" <td>3.469451</td>\n",
" <td>0.663080</td>\n",
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" <tr>\n",
" <th>853135</th>\n",
" <td>2018-04-01 00:10:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.996000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>3.070866</td>\n",
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" <td>0.0</td>\n",
" <td>3.354114</td>\n",
" <td>0.540446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853136</th>\n",
" <td>2018-04-01 00:15:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
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" <td>39.680702</td>\n",
" <td>3.038623</td>\n",
" <td>1.482489</td>\n",
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" <td>0.0</td>\n",
" <td>3.365968</td>\n",
" <td>0.597973</td>\n",
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" <tr>\n",
" <th>853137</th>\n",
" <td>2018-04-01 00:20:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.387363</td>\n",
" <td>39.319881</td>\n",
" <td>2.656474</td>\n",
" <td>1.134153</td>\n",
" <td>66.430733</td>\n",
" <td>0.0</td>\n",
" <td>3.222809</td>\n",
" <td>0.530707</td>\n",
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" <tr>\n",
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" <th>958248</th>\n",
" <td>2019-03-31 23:35:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.991333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.303740</td>\n",
" <td>34.212711</td>\n",
" <td>1.210789</td>\n",
" <td>0.787026</td>\n",
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" <td>0.0</td>\n",
" <td>3.271109</td>\n",
" <td>0.476681</td>\n",
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" <tr>\n",
" <th>958249</th>\n",
" <td>2019-03-31 23:40:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
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" <td>0.444219</td>\n",
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" <tr>\n",
" <th>958250</th>\n",
" <td>2019-03-31 23:45:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>2.873335</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>958252</th>\n",
" <td>2019-03-31 23:55:00</td>\n",
" <td>29021.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.134816</td>\n",
" <td>34.500034</td>\n",
" <td>1.043269</td>\n",
" <td>0.597816</td>\n",
" <td>34.228458</td>\n",
" <td>0.0</td>\n",
" <td>2.947993</td>\n",
" <td>0.294085</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105120 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp Active_Energy_Delivered_Received \\\n",
"853133 2018-04-01 00:00:00 18104.0 \n",
"853134 2018-04-01 00:05:00 18104.0 \n",
"853135 2018-04-01 00:10:00 18104.0 \n",
"853136 2018-04-01 00:15:00 18104.0 \n",
"853137 2018-04-01 00:20:00 18104.0 \n",
"... ... ... \n",
"958248 2019-03-31 23:35:00 29021.0 \n",
"958249 2019-03-31 23:40:00 29021.0 \n",
"958250 2019-03-31 23:45:00 29021.0 \n",
"958251 2019-03-31 23:50:00 29021.0 \n",
"958252 2019-03-31 23:55:00 29021.0 \n",
"\n",
" Current_Phase_Average Active_Power Performance_Ratio Wind_Speed \\\n",
"853133 0.997333 0.0 0.0 NaN \n",
"853134 0.997333 0.0 0.0 NaN \n",
"853135 0.996000 0.0 0.0 NaN \n",
"853136 1.000000 0.0 0.0 NaN \n",
"853137 1.000000 0.0 0.0 NaN \n",
"... ... ... ... ... \n",
"958248 0.991333 0.0 0.0 NaN \n",
"958249 0.995333 0.0 0.0 NaN \n",
"958250 0.995333 0.0 0.0 NaN \n",
"958251 0.999333 0.0 0.0 NaN \n",
"958252 1.000000 0.0 0.0 NaN \n",
"\n",
" Weather_Temperature_Celsius Weather_Relative_Humidity \\\n",
"853133 19.779453 40.025826 \n",
"853134 19.714937 39.605961 \n",
"853135 19.549330 39.608631 \n",
"853136 19.405870 39.680702 \n",
"853137 19.387363 39.319881 \n",
"... ... ... \n",
"958248 13.303740 34.212711 \n",
"958249 13.120920 34.394939 \n",
"958250 12.879215 35.167400 \n",
"958251 12.915867 35.359989 \n",
"958252 13.134816 34.500034 \n",
"\n",
" Global_Horizontal_Radiation Diffuse_Horizontal_Radiation \\\n",
"853133 3.232706 1.690531 \n",
"853134 3.194991 1.576346 \n",
"853135 3.070866 1.576157 \n",
"853136 3.038623 1.482489 \n",
"853137 2.656474 1.134153 \n",
"... ... ... \n",
"958248 1.210789 0.787026 \n",
"958249 2.142980 1.582670 \n",
"958250 1.926214 1.545889 \n",
"958251 1.317695 0.851529 \n",
"958252 1.043269 0.597816 \n",
"\n",
" Wind_Direction Weather_Daily_Rainfall Radiation_Global_Tilted \\\n",
"853133 64.372742 0.0 3.565593 \n",
"853134 65.954178 0.0 3.469451 \n",
"853135 65.347725 0.0 3.354114 \n",
"853136 67.103271 0.0 3.365968 \n",
"853137 66.430733 0.0 3.222809 \n",
"... ... ... ... \n",
"958248 34.165325 0.0 3.271109 \n",
"958249 34.202522 0.0 3.163039 \n",
"958250 34.233902 0.0 3.197096 \n",
"958251 34.308563 0.0 2.873335 \n",
"958252 34.228458 0.0 2.947993 \n",
"\n",
" Radiation_Diffuse_Tilted \n",
"853133 0.742383 \n",
"853134 0.663080 \n",
"853135 0.540446 \n",
"853136 0.597973 \n",
"853137 0.530707 \n",
"... ... \n",
"958248 0.476681 \n",
"958249 0.444219 \n",
"958250 0.475794 \n",
"958251 0.320598 \n",
"958252 0.294085 \n",
"\n",
"[105120 rows x 14 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,\n",
" <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,\n",
" <Axes: >], dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1200x800 with 13 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data2.plot(legend=True, subplots=True, figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 105120 entries, 853133 to 958252\n",
"Data columns (total 14 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 timestamp 105120 non-null object \n",
" 1 Active_Energy_Delivered_Received 104221 non-null float64\n",
" 2 Current_Phase_Average 104221 non-null float64\n",
" 3 Active_Power 104221 non-null float64\n",
" 4 Performance_Ratio 104221 non-null float64\n",
" 5 Wind_Speed 0 non-null float64\n",
" 6 Weather_Temperature_Celsius 105120 non-null float64\n",
" 7 Weather_Relative_Humidity 105120 non-null float64\n",
" 8 Global_Horizontal_Radiation 105120 non-null float64\n",
" 9 Diffuse_Horizontal_Radiation 105120 non-null float64\n",
" 10 Wind_Direction 105120 non-null float64\n",
" 11 Weather_Daily_Rainfall 105120 non-null float64\n",
" 12 Radiation_Global_Tilted 103998 non-null float64\n",
" 13 Radiation_Diffuse_Tilted 103998 non-null float64\n",
"dtypes: float64(13), object(1)\n",
"memory usage: 11.2+ MB\n"
]
}
],
"source": [
"data2.info()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"for dataset in [data2]:\n",
" dataset.columns=['time','AE_Power','Current','Power','PR','Wind_speed','Temp','Humidity','GHI','DHI','Wind_dir','Rainfall','RGT','RDT']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <th>time</th>\n",
" <th>AE_Power</th>\n",
" <th>Current</th>\n",
" <th>Power</th>\n",
" <th>PR</th>\n",
" <th>Wind_speed</th>\n",
" <th>Temp</th>\n",
" <th>Humidity</th>\n",
" <th>GHI</th>\n",
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" <th>RGT</th>\n",
" <th>RDT</th>\n",
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" <tr>\n",
" <th>853133</th>\n",
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" <th>853134</th>\n",
" <td>2018-04-01 00:05:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>19.714937</td>\n",
" <td>39.605961</td>\n",
" <td>3.194991</td>\n",
" <td>1.576346</td>\n",
" <td>65.954178</td>\n",
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" <td>3.469451</td>\n",
" <td>0.663080</td>\n",
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" <tr>\n",
" <th>853135</th>\n",
" <td>2018-04-01 00:10:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.996000</td>\n",
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" <td>3.354114</td>\n",
" <td>0.540446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853136</th>\n",
" <td>2018-04-01 00:15:00</td>\n",
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" <td>19.405870</td>\n",
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" <td>67.103271</td>\n",
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" <td>2018-04-01 00:20:00</td>\n",
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" <td>NaN</td>\n",
" <td>19.387363</td>\n",
" <td>39.319881</td>\n",
" <td>2.656474</td>\n",
" <td>1.134153</td>\n",
" <td>66.430733</td>\n",
" <td>0.0</td>\n",
" <td>3.222809</td>\n",
" <td>0.530707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <th>958248</th>\n",
" <td>2019-03-31 23:35:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.991333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>13.303740</td>\n",
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" <td>1.210789</td>\n",
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" <td>0.476681</td>\n",
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" <tr>\n",
" <th>958249</th>\n",
" <td>2019-03-31 23:40:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.120920</td>\n",
" <td>34.394939</td>\n",
" <td>2.142980</td>\n",
" <td>1.582670</td>\n",
" <td>34.202522</td>\n",
" <td>0.0</td>\n",
" <td>3.163039</td>\n",
" <td>0.444219</td>\n",
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" <tr>\n",
" <th>958250</th>\n",
" <td>2019-03-31 23:45:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>12.879215</td>\n",
" <td>35.167400</td>\n",
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" <tr>\n",
" <th>958251</th>\n",
" <td>2019-03-31 23:50:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.999333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>12.915867</td>\n",
" <td>35.359989</td>\n",
" <td>1.317695</td>\n",
" <td>0.851529</td>\n",
" <td>34.308563</td>\n",
" <td>0.0</td>\n",
" <td>2.873335</td>\n",
" <td>0.320598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958252</th>\n",
" <td>2019-03-31 23:55:00</td>\n",
" <td>29021.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.134816</td>\n",
" <td>34.500034</td>\n",
" <td>1.043269</td>\n",
" <td>0.597816</td>\n",
" <td>34.228458</td>\n",
" <td>0.0</td>\n",
" <td>2.947993</td>\n",
" <td>0.294085</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105120 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" time AE_Power Current Power PR Wind_speed \\\n",
"853133 2018-04-01 00:00:00 18104.0 0.997333 0.0 0.0 NaN \n",
"853134 2018-04-01 00:05:00 18104.0 0.997333 0.0 0.0 NaN \n",
"853135 2018-04-01 00:10:00 18104.0 0.996000 0.0 0.0 NaN \n",
"853136 2018-04-01 00:15:00 18104.0 1.000000 0.0 0.0 NaN \n",
"853137 2018-04-01 00:20:00 18104.0 1.000000 0.0 0.0 NaN \n",
"... ... ... ... ... ... ... \n",
"958248 2019-03-31 23:35:00 29021.0 0.991333 0.0 0.0 NaN \n",
"958249 2019-03-31 23:40:00 29021.0 0.995333 0.0 0.0 NaN \n",
"958250 2019-03-31 23:45:00 29021.0 0.995333 0.0 0.0 NaN \n",
"958251 2019-03-31 23:50:00 29021.0 0.999333 0.0 0.0 NaN \n",
"958252 2019-03-31 23:55:00 29021.0 1.000000 0.0 0.0 NaN \n",
"\n",
" Temp Humidity GHI DHI Wind_dir Rainfall \\\n",
"853133 19.779453 40.025826 3.232706 1.690531 64.372742 0.0 \n",
"853134 19.714937 39.605961 3.194991 1.576346 65.954178 0.0 \n",
"853135 19.549330 39.608631 3.070866 1.576157 65.347725 0.0 \n",
"853136 19.405870 39.680702 3.038623 1.482489 67.103271 0.0 \n",
"853137 19.387363 39.319881 2.656474 1.134153 66.430733 0.0 \n",
"... ... ... ... ... ... ... \n",
"958248 13.303740 34.212711 1.210789 0.787026 34.165325 0.0 \n",
"958249 13.120920 34.394939 2.142980 1.582670 34.202522 0.0 \n",
"958250 12.879215 35.167400 1.926214 1.545889 34.233902 0.0 \n",
"958251 12.915867 35.359989 1.317695 0.851529 34.308563 0.0 \n",
"958252 13.134816 34.500034 1.043269 0.597816 34.228458 0.0 \n",
"\n",
" RGT RDT \n",
"853133 3.565593 0.742383 \n",
"853134 3.469451 0.663080 \n",
"853135 3.354114 0.540446 \n",
"853136 3.365968 0.597973 \n",
"853137 3.222809 0.530707 \n",
"... ... ... \n",
"958248 3.271109 0.476681 \n",
"958249 3.163039 0.444219 \n",
"958250 3.197096 0.475794 \n",
"958251 2.873335 0.320598 \n",
"958252 2.947993 0.294085 \n",
"\n",
"[105120 rows x 14 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" time AE_Power Current PR Wind_speed Temp \\\n",
"853133 2018-04-01 00:00:00 18104.0 0.997333 0.0 NaN 19.779453 \n",
"853134 2018-04-01 00:05:00 18104.0 0.997333 0.0 NaN 19.714937 \n",
"853135 2018-04-01 00:10:00 18104.0 0.996000 0.0 NaN 19.549330 \n",
"853136 2018-04-01 00:15:00 18104.0 1.000000 0.0 NaN 19.405870 \n",
"853137 2018-04-01 00:20:00 18104.0 1.000000 0.0 NaN 19.387363 \n",
"... ... ... ... ... ... ... \n",
"958248 2019-03-31 23:35:00 29021.0 0.991333 0.0 NaN 13.303740 \n",
"958249 2019-03-31 23:40:00 29021.0 0.995333 0.0 NaN 13.120920 \n",
"958250 2019-03-31 23:45:00 29021.0 0.995333 0.0 NaN 12.879215 \n",
"958251 2019-03-31 23:50:00 29021.0 0.999333 0.0 NaN 12.915867 \n",
"958252 2019-03-31 23:55:00 29021.0 1.000000 0.0 NaN 13.134816 \n",
"\n",
" Humidity GHI DHI Wind_dir Rainfall RGT \\\n",
"853133 40.025826 3.232706 1.690531 64.372742 0.0 3.565593 \n",
"853134 39.605961 3.194991 1.576346 65.954178 0.0 3.469451 \n",
"853135 39.608631 3.070866 1.576157 65.347725 0.0 3.354114 \n",
"853136 39.680702 3.038623 1.482489 67.103271 0.0 3.365968 \n",
"853137 39.319881 2.656474 1.134153 66.430733 0.0 3.222809 \n",
"... ... ... ... ... ... ... \n",
"958248 34.212711 1.210789 0.787026 34.165325 0.0 3.271109 \n",
"958249 34.394939 2.142980 1.582670 34.202522 0.0 3.163039 \n",
"958250 35.167400 1.926214 1.545889 34.233902 0.0 3.197096 \n",
"958251 35.359989 1.317695 0.851529 34.308563 0.0 2.873335 \n",
"958252 34.500034 1.043269 0.597816 34.228458 0.0 2.947993 \n",
"\n",
" RDT Power \n",
"853133 0.742383 0.0 \n",
"853134 0.663080 0.0 \n",
"853135 0.540446 0.0 \n",
"853136 0.597973 0.0 \n",
"853137 0.530707 0.0 \n",
"... ... ... \n",
"958248 0.476681 0.0 \n",
"958249 0.444219 0.0 \n",
"958250 0.475794 0.0 \n",
"958251 0.320598 0.0 \n",
"958252 0.294085 0.0 \n",
"\n",
"[105120 rows x 14 columns]\n"
]
}
],
"source": [
"df = pd.DataFrame(data2)\n",
"\n",
"# 将'Power'列移到最后一列\n",
"columns = df.columns.tolist() # 获取列名列表\n",
"columns.remove('Power') # 移除'Power'列\n",
"columns.append('Power') # 将'Power'列添加到列名列表的末尾\n",
"\n",
"# 使用重新排列后的列名重新构建DataFrame\n",
"df = df[columns]\n",
"\n",
"# 打印结果，确认'Power'列已经移到最后\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>AE_Power</th>\n",
" <th>Current</th>\n",
" <th>PR</th>\n",
" <th>Wind_speed</th>\n",
" <th>Temp</th>\n",
" <th>Humidity</th>\n",
" <th>GHI</th>\n",
" <th>DHI</th>\n",
" <th>Wind_dir</th>\n",
" <th>Rainfall</th>\n",
" <th>RGT</th>\n",
" <th>RDT</th>\n",
" <th>Power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>853133</th>\n",
" <td>2018-04-01 00:00:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.779453</td>\n",
" <td>40.025826</td>\n",
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" <td>0.742383</td>\n",
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" <tr>\n",
" <th>853134</th>\n",
" <td>2018-04-01 00:05:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.714937</td>\n",
" <td>39.605961</td>\n",
" <td>3.194991</td>\n",
" <td>1.576346</td>\n",
" <td>65.954178</td>\n",
" <td>0.0</td>\n",
" <td>3.469451</td>\n",
" <td>0.663080</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853135</th>\n",
" <td>2018-04-01 00:10:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.996000</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.549330</td>\n",
" <td>39.608631</td>\n",
" <td>3.070866</td>\n",
" <td>1.576157</td>\n",
" <td>65.347725</td>\n",
" <td>0.0</td>\n",
" <td>3.354114</td>\n",
" <td>0.540446</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853136</th>\n",
" <td>2018-04-01 00:15:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.405870</td>\n",
" <td>39.680702</td>\n",
" <td>3.038623</td>\n",
" <td>1.482489</td>\n",
" <td>67.103271</td>\n",
" <td>0.0</td>\n",
" <td>3.365968</td>\n",
" <td>0.597973</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853137</th>\n",
" <td>2018-04-01 00:20:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>19.387363</td>\n",
" <td>39.319881</td>\n",
" <td>2.656474</td>\n",
" <td>1.134153</td>\n",
" <td>66.430733</td>\n",
" <td>0.0</td>\n",
" <td>3.222809</td>\n",
" <td>0.530707</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958248</th>\n",
" <td>2019-03-31 23:35:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.991333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.303740</td>\n",
" <td>34.212711</td>\n",
" <td>1.210789</td>\n",
" <td>0.787026</td>\n",
" <td>34.165325</td>\n",
" <td>0.0</td>\n",
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" <td>0.476681</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958249</th>\n",
" <td>2019-03-31 23:40:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.120920</td>\n",
" <td>34.394939</td>\n",
" <td>2.142980</td>\n",
" <td>1.582670</td>\n",
" <td>34.202522</td>\n",
" <td>0.0</td>\n",
" <td>3.163039</td>\n",
" <td>0.444219</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958250</th>\n",
" <td>2019-03-31 23:45:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>12.879215</td>\n",
" <td>35.167400</td>\n",
" <td>1.926214</td>\n",
" <td>1.545889</td>\n",
" <td>34.233902</td>\n",
" <td>0.0</td>\n",
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" <td>0.475794</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>958251</th>\n",
" <td>2019-03-31 23:50:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.999333</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>12.915867</td>\n",
" <td>35.359989</td>\n",
" <td>1.317695</td>\n",
" <td>0.851529</td>\n",
" <td>34.308563</td>\n",
" <td>0.0</td>\n",
" <td>2.873335</td>\n",
" <td>0.320598</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958252</th>\n",
" <td>2019-03-31 23:55:00</td>\n",
" <td>29021.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>13.134816</td>\n",
" <td>34.500034</td>\n",
" <td>1.043269</td>\n",
" <td>0.597816</td>\n",
" <td>34.228458</td>\n",
" <td>0.0</td>\n",
" <td>2.947993</td>\n",
" <td>0.294085</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105120 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" time AE_Power Current PR Wind_speed Temp \\\n",
"853133 2018-04-01 00:00:00 18104.0 0.997333 0.0 NaN 19.779453 \n",
"853134 2018-04-01 00:05:00 18104.0 0.997333 0.0 NaN 19.714937 \n",
"853135 2018-04-01 00:10:00 18104.0 0.996000 0.0 NaN 19.549330 \n",
"853136 2018-04-01 00:15:00 18104.0 1.000000 0.0 NaN 19.405870 \n",
"853137 2018-04-01 00:20:00 18104.0 1.000000 0.0 NaN 19.387363 \n",
"... ... ... ... ... ... ... \n",
"958248 2019-03-31 23:35:00 29021.0 0.991333 0.0 NaN 13.303740 \n",
"958249 2019-03-31 23:40:00 29021.0 0.995333 0.0 NaN 13.120920 \n",
"958250 2019-03-31 23:45:00 29021.0 0.995333 0.0 NaN 12.879215 \n",
"958251 2019-03-31 23:50:00 29021.0 0.999333 0.0 NaN 12.915867 \n",
"958252 2019-03-31 23:55:00 29021.0 1.000000 0.0 NaN 13.134816 \n",
"\n",
" Humidity GHI DHI Wind_dir Rainfall RGT \\\n",
"853133 40.025826 3.232706 1.690531 64.372742 0.0 3.565593 \n",
"853134 39.605961 3.194991 1.576346 65.954178 0.0 3.469451 \n",
"853135 39.608631 3.070866 1.576157 65.347725 0.0 3.354114 \n",
"853136 39.680702 3.038623 1.482489 67.103271 0.0 3.365968 \n",
"853137 39.319881 2.656474 1.134153 66.430733 0.0 3.222809 \n",
"... ... ... ... ... ... ... \n",
"958248 34.212711 1.210789 0.787026 34.165325 0.0 3.271109 \n",
"958249 34.394939 2.142980 1.582670 34.202522 0.0 3.163039 \n",
"958250 35.167400 1.926214 1.545889 34.233902 0.0 3.197096 \n",
"958251 35.359989 1.317695 0.851529 34.308563 0.0 2.873335 \n",
"958252 34.500034 1.043269 0.597816 34.228458 0.0 2.947993 \n",
"\n",
" RDT Power \n",
"853133 0.742383 0.0 \n",
"853134 0.663080 0.0 \n",
"853135 0.540446 0.0 \n",
"853136 0.597973 0.0 \n",
"853137 0.530707 0.0 \n",
"... ... ... \n",
"958248 0.476681 0.0 \n",
"958249 0.444219 0.0 \n",
"958250 0.475794 0.0 \n",
"958251 0.320598 0.0 \n",
"958252 0.294085 0.0 \n",
"\n",
"[105120 rows x 14 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 105120 entries, 853133 to 958252\n",
"Data columns (total 14 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 time 105120 non-null object \n",
" 1 AE_Power 104221 non-null float64\n",
" 2 Current 104221 non-null float64\n",
" 3 PR 104221 non-null float64\n",
" 4 Wind_speed 0 non-null float64\n",
" 5 Temp 105120 non-null float64\n",
" 6 Humidity 105120 non-null float64\n",
" 7 GHI 105120 non-null float64\n",
" 8 DHI 105120 non-null float64\n",
" 9 Wind_dir 105120 non-null float64\n",
" 10 Rainfall 105120 non-null float64\n",
" 11 RGT 103998 non-null float64\n",
" 12 RDT 103998 non-null float64\n",
" 13 Power 104221 non-null float64\n",
"dtypes: float64(13), object(1)\n",
"memory usage: 11.2+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# # 打印出所有包含 NaN 的位置\n",
"# nan_positions = data2.isna()\n",
"\n",
"# print(\"Positions of NaN values:\")\n",
"# print(nan_positions)\n",
"# # 将处理后的 DataFrame 保存为 Excel 文件\n",
"# excel_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\\processed_data.xlsx' # 定义 Excel 文件路径和文件名\n",
"\n",
"# nan_positions.to_excel(excel_file_path, index=False) # 将 DataFrame 保存为 Excel 文件，不包含索引"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"data3 = df.drop(\"Wind_speed\", axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,\n",
" <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
" dtype=object)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib as plt\n",
"data3.plot(legend=True, subplots=True, figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>time</th>\n",
" <th>AE_Power</th>\n",
" <th>Current</th>\n",
" <th>PR</th>\n",
" <th>Temp</th>\n",
" <th>Humidity</th>\n",
" <th>GHI</th>\n",
" <th>DHI</th>\n",
" <th>Wind_dir</th>\n",
" <th>Rainfall</th>\n",
" <th>RGT</th>\n",
" <th>RDT</th>\n",
" <th>Power</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>853133</th>\n",
" <td>2018-04-01 00:00:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>19.779453</td>\n",
" <td>40.025826</td>\n",
" <td>3.232706</td>\n",
" <td>1.690531</td>\n",
" <td>64.372742</td>\n",
" <td>0.0</td>\n",
" <td>3.565593</td>\n",
" <td>0.742383</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853134</th>\n",
" <td>2018-04-01 00:05:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.997333</td>\n",
" <td>0.0</td>\n",
" <td>19.714937</td>\n",
" <td>39.605961</td>\n",
" <td>3.194991</td>\n",
" <td>1.576346</td>\n",
" <td>65.954178</td>\n",
" <td>0.0</td>\n",
" <td>3.469451</td>\n",
" <td>0.663080</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853135</th>\n",
" <td>2018-04-01 00:10:00</td>\n",
" <td>18104.0</td>\n",
" <td>0.996000</td>\n",
" <td>0.0</td>\n",
" <td>19.549330</td>\n",
" <td>39.608631</td>\n",
" <td>3.070866</td>\n",
" <td>1.576157</td>\n",
" <td>65.347725</td>\n",
" <td>0.0</td>\n",
" <td>3.354114</td>\n",
" <td>0.540446</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853136</th>\n",
" <td>2018-04-01 00:15:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>19.405870</td>\n",
" <td>39.680702</td>\n",
" <td>3.038623</td>\n",
" <td>1.482489</td>\n",
" <td>67.103271</td>\n",
" <td>0.0</td>\n",
" <td>3.365968</td>\n",
" <td>0.597973</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>853137</th>\n",
" <td>2018-04-01 00:20:00</td>\n",
" <td>18104.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>19.387363</td>\n",
" <td>39.319881</td>\n",
" <td>2.656474</td>\n",
" <td>1.134153</td>\n",
" <td>66.430733</td>\n",
" <td>0.0</td>\n",
" <td>3.222809</td>\n",
" <td>0.530707</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958248</th>\n",
" <td>2019-03-31 23:35:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.991333</td>\n",
" <td>0.0</td>\n",
" <td>13.303740</td>\n",
" <td>34.212711</td>\n",
" <td>1.210789</td>\n",
" <td>0.787026</td>\n",
" <td>34.165325</td>\n",
" <td>0.0</td>\n",
" <td>3.271109</td>\n",
" <td>0.476681</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958249</th>\n",
" <td>2019-03-31 23:40:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>13.120920</td>\n",
" <td>34.394939</td>\n",
" <td>2.142980</td>\n",
" <td>1.582670</td>\n",
" <td>34.202522</td>\n",
" <td>0.0</td>\n",
" <td>3.163039</td>\n",
" <td>0.444219</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958250</th>\n",
" <td>2019-03-31 23:45:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.995333</td>\n",
" <td>0.0</td>\n",
" <td>12.879215</td>\n",
" <td>35.167400</td>\n",
" <td>1.926214</td>\n",
" <td>1.545889</td>\n",
" <td>34.233902</td>\n",
" <td>0.0</td>\n",
" <td>3.197096</td>\n",
" <td>0.475794</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958251</th>\n",
" <td>2019-03-31 23:50:00</td>\n",
" <td>29021.0</td>\n",
" <td>0.999333</td>\n",
" <td>0.0</td>\n",
" <td>12.915867</td>\n",
" <td>35.359989</td>\n",
" <td>1.317695</td>\n",
" <td>0.851529</td>\n",
" <td>34.308563</td>\n",
" <td>0.0</td>\n",
" <td>2.873335</td>\n",
" <td>0.320598</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958252</th>\n",
" <td>2019-03-31 23:55:00</td>\n",
" <td>29021.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>13.134816</td>\n",
" <td>34.500034</td>\n",
" <td>1.043269</td>\n",
" <td>0.597816</td>\n",
" <td>34.228458</td>\n",
" <td>0.0</td>\n",
" <td>2.947993</td>\n",
" <td>0.294085</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105120 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" time AE_Power Current PR Temp Humidity \\\n",
"853133 2018-04-01 00:00:00 18104.0 0.997333 0.0 19.779453 40.025826 \n",
"853134 2018-04-01 00:05:00 18104.0 0.997333 0.0 19.714937 39.605961 \n",
"853135 2018-04-01 00:10:00 18104.0 0.996000 0.0 19.549330 39.608631 \n",
"853136 2018-04-01 00:15:00 18104.0 1.000000 0.0 19.405870 39.680702 \n",
"853137 2018-04-01 00:20:00 18104.0 1.000000 0.0 19.387363 39.319881 \n",
"... ... ... ... ... ... ... \n",
"958248 2019-03-31 23:35:00 29021.0 0.991333 0.0 13.303740 34.212711 \n",
"958249 2019-03-31 23:40:00 29021.0 0.995333 0.0 13.120920 34.394939 \n",
"958250 2019-03-31 23:45:00 29021.0 0.995333 0.0 12.879215 35.167400 \n",
"958251 2019-03-31 23:50:00 29021.0 0.999333 0.0 12.915867 35.359989 \n",
"958252 2019-03-31 23:55:00 29021.0 1.000000 0.0 13.134816 34.500034 \n",
"\n",
" GHI DHI Wind_dir Rainfall RGT RDT Power \n",
"853133 3.232706 1.690531 64.372742 0.0 3.565593 0.742383 0.0 \n",
"853134 3.194991 1.576346 65.954178 0.0 3.469451 0.663080 0.0 \n",
"853135 3.070866 1.576157 65.347725 0.0 3.354114 0.540446 0.0 \n",
"853136 3.038623 1.482489 67.103271 0.0 3.365968 0.597973 0.0 \n",
"853137 2.656474 1.134153 66.430733 0.0 3.222809 0.530707 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"958248 1.210789 0.787026 34.165325 0.0 3.271109 0.476681 0.0 \n",
"958249 2.142980 1.582670 34.202522 0.0 3.163039 0.444219 0.0 \n",
"958250 1.926214 1.545889 34.233902 0.0 3.197096 0.475794 0.0 \n",
"958251 1.317695 0.851529 34.308563 0.0 2.873335 0.320598 0.0 \n",
"958252 1.043269 0.597816 34.228458 0.0 2.947993 0.294085 0.0 \n",
"\n",
"[105120 rows x 13 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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\\data3.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\\data3.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": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >,\n",
" <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >],\n",
" dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x800 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib as plt\n",
"data3.plot(legend=True, subplots=True, figsize=(12, 8))"
]
},
{
"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
}