An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network
As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differen...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1501963/full |
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| author | Xiaofeng Yang Shousheng Zhao Kangyi Li Wenjin Chen Si Zhang Jingwei Chen |
| author_facet | Xiaofeng Yang Shousheng Zhao Kangyi Li Wenjin Chen Si Zhang Jingwei Chen |
| author_sort | Xiaofeng Yang |
| collection | DOAJ |
| description | As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy. |
| format | Article |
| id | doaj-art-15b8583a1f634df6b477b9b5a2da7337 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-15b8583a1f634df6b477b9b5a2da73372025-01-22T11:38:42ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.15019631501963An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural networkXiaofeng YangShousheng ZhaoKangyi LiWenjin ChenSi ZhangJingwei ChenAs renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1501963/fullshort-term load forecastingfused featuresprophet algorithmSHAP algorithmconvlstm-3D model |
| spellingShingle | Xiaofeng Yang Shousheng Zhao Kangyi Li Wenjin Chen Si Zhang Jingwei Chen An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network Frontiers in Energy Research short-term load forecasting fused features prophet algorithm SHAP algorithm convlstm-3D model |
| title | An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network |
| title_full | An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network |
| title_fullStr | An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network |
| title_full_unstemmed | An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network |
| title_short | An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network |
| title_sort | optimized method for short term load forecasting based on feature fusion and convlstm 3d neural network |
| topic | short-term load forecasting fused features prophet algorithm SHAP algorithm convlstm-3D model |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1501963/full |
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