Short-term load forecasting method for power system based on key feature optimization
Accurate forecasting of short-term power load is an important condition for the safe and economic operation of the power system. To improve the accuracy of short-term load forecasting for the power system， a short-term load forecasting method based on key feature optimization is proposed. Firstly， t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2023-08-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=2477c590-062b-45e9-8612-3aec401f3b56 |
| _version_ | 1827856341315616768 |
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| author | ZHU Geng WANG Bo HE Xu YU Yinshu BAI Wenbo |
| author_facet | ZHU Geng WANG Bo HE Xu YU Yinshu BAI Wenbo |
| author_sort | ZHU Geng |
| collection | DOAJ |
| description | Accurate forecasting of short-term power load is an important condition for the safe and economic operation of the power system. To improve the accuracy of short-term load forecasting for the power system， a short-term load forecasting method based on key feature optimization is proposed. Firstly， the construction method of the meteorological features， daily type features and historical load features affecting the short-term load of the power system is optimized， which can provide more prior knowledge for the load forecasting model. Then， considering the characteristics of the input features and the output prediction vector， a short-term power load forecasting model combining the convolutional neural network and the fully connected layer is constructed. Finally， the effect of the short-term load forecasting method for the power system based on the key feature optimization in the actual load forecasting task is validated by a numerical example. The example result shows that the key feature optimization of meteorological features， daily type features and historical load features is conducive to improving the accuracy of the short-term load forecasting for the power system. |
| first_indexed | 2024-03-12T12:24:22Z |
| format | Article |
| id | doaj.art-121d83eb223e46f69b6fcb5c7b967605 |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-03-12T12:24:22Z |
| publishDate | 2023-08-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-121d83eb223e46f69b6fcb5c7b9676052023-08-30T00:46:03Zzhozhejiang electric powerZhejiang dianli1007-18812023-08-01428465310.19585/j.zjdl.2023080061007-1881（2023）08-0046-08Short-term load forecasting method for power system based on key feature optimizationZHU Geng0WANG Bo1HE Xu2YU Yinshu3BAI Wenbo4State Grid Ningbo Power Supply Company, Ningbo, Zhejiang 315000, ChinaState Grid Ningbo Power Supply Company, Ningbo, Zhejiang 315000, ChinaState Grid Ningbo Power Supply Company, Ningbo, Zhejiang 315000, ChinaState Grid Ningbo Power Supply Company, Ningbo, Zhejiang 315000, ChinaNingbo Electric Power Design Institute Co., Ltd., Ningbo, Zhejiang 315000, ChinaAccurate forecasting of short-term power load is an important condition for the safe and economic operation of the power system. To improve the accuracy of short-term load forecasting for the power system， a short-term load forecasting method based on key feature optimization is proposed. Firstly， the construction method of the meteorological features， daily type features and historical load features affecting the short-term load of the power system is optimized， which can provide more prior knowledge for the load forecasting model. Then， considering the characteristics of the input features and the output prediction vector， a short-term power load forecasting model combining the convolutional neural network and the fully connected layer is constructed. Finally， the effect of the short-term load forecasting method for the power system based on the key feature optimization in the actual load forecasting task is validated by a numerical example. The example result shows that the key feature optimization of meteorological features， daily type features and historical load features is conducive to improving the accuracy of the short-term load forecasting for the power system.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=2477c590-062b-45e9-8612-3aec401f3b56feature optimizationload forecastingconvolutional neural networkfully connected layer |
| spellingShingle | ZHU Geng WANG Bo HE Xu YU Yinshu BAI Wenbo Short-term load forecasting method for power system based on key feature optimization Zhejiang dianli feature optimization load forecasting convolutional neural network fully connected layer |
| title | Short-term load forecasting method for power system based on key feature optimization |
| title_full | Short-term load forecasting method for power system based on key feature optimization |
| title_fullStr | Short-term load forecasting method for power system based on key feature optimization |
| title_full_unstemmed | Short-term load forecasting method for power system based on key feature optimization |
| title_short | Short-term load forecasting method for power system based on key feature optimization |
| title_sort | short term load forecasting method for power system based on key feature optimization |
| topic | feature optimization load forecasting convolutional neural network fully connected layer |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=2477c590-062b-45e9-8612-3aec401f3b56 |
| work_keys_str_mv | AT zhugeng shorttermloadforecastingmethodforpowersystembasedonkeyfeatureoptimization AT wangbo shorttermloadforecastingmethodforpowersystembasedonkeyfeatureoptimization AT hexu shorttermloadforecastingmethodforpowersystembasedonkeyfeatureoptimization AT yuyinshu shorttermloadforecastingmethodforpowersystembasedonkeyfeatureoptimization AT baiwenbo shorttermloadforecastingmethodforpowersystembasedonkeyfeatureoptimization |