Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer
Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting. The method integrates multi-frequency sequence feature analysis and multi-point correcti...
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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.2024.1524319/full |
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| author | Kaiyuan Hou Xiaotian Zhang Junjie Yang Jiyun Hu Guangzhi Yao Jiannan Zhang |
| author_facet | Kaiyuan Hou Xiaotian Zhang Junjie Yang Jiyun Hu Guangzhi Yao Jiannan Zhang |
| author_sort | Kaiyuan Hou |
| collection | DOAJ |
| description | Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting. The method integrates multi-frequency sequence feature analysis and multi-point correction using the FEDformer model. Initially, variational mode decomposition (VMD) technology decomposes the load sequence into multiple subsequences, each exhibiting distinct frequency characteristics. Subsequently, for each frequency band of the load sequence, the LightGBM algorithm quantifies the correlation between the load and various influencing factors. The filtered features are then input into the FEDformer model, providing preliminary short-term and long-term sequence prediction results. Finally, a point-by-point forecasting method based on a tree model generates multi-point load prediction results by training multiple LightGBM models. Throughout the forecasting process, a weighted threshold α is set, and a hybrid weighting method is utilized to combine the forecast results from different models, culminating in the final short-term load forecast results. Validation of the proposed hybrid model was conducted on an actual dataset from a specific area, The results exhibit higher prediction accuracy, affirming the proposed method as a novel and effective approach for short-term load forecasting. |
| format | Article |
| id | doaj-art-0146d934b4e14e5fb6d8febf79a10eb6 |
| 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-0146d934b4e14e5fb6d8febf79a10eb62025-01-29T06:45:37ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.15243191524319Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformerKaiyuan Hou0Xiaotian Zhang1Junjie Yang2Jiyun Hu3Guangzhi Yao4Jiannan Zhang5Northeast Branch of State Grid Corporation of China, Shenyang, ChinaNortheast Branch of State Grid Corporation of China, Shenyang, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, ChinaNortheast Branch of State Grid Corporation of China, Shenyang, ChinaNortheast Branch of State Grid Corporation of China, Shenyang, ChinaNortheast Branch of State Grid Corporation of China, Shenyang, ChinaGiven the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting. The method integrates multi-frequency sequence feature analysis and multi-point correction using the FEDformer model. Initially, variational mode decomposition (VMD) technology decomposes the load sequence into multiple subsequences, each exhibiting distinct frequency characteristics. Subsequently, for each frequency band of the load sequence, the LightGBM algorithm quantifies the correlation between the load and various influencing factors. The filtered features are then input into the FEDformer model, providing preliminary short-term and long-term sequence prediction results. Finally, a point-by-point forecasting method based on a tree model generates multi-point load prediction results by training multiple LightGBM models. Throughout the forecasting process, a weighted threshold α is set, and a hybrid weighting method is utilized to combine the forecast results from different models, culminating in the final short-term load forecast results. Validation of the proposed hybrid model was conducted on an actual dataset from a specific area, The results exhibit higher prediction accuracy, affirming the proposed method as a novel and effective approach for short-term load forecasting.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1524319/fullshort-term load forecastingFEDformerVMDtime series forecastinglightgbmmulti-point modify |
| spellingShingle | Kaiyuan Hou Xiaotian Zhang Junjie Yang Jiyun Hu Guangzhi Yao Jiannan Zhang Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer Frontiers in Energy Research short-term load forecasting FEDformer VMD time series forecasting lightgbm multi-point modify |
| title | Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer |
| title_full | Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer |
| title_fullStr | Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer |
| title_full_unstemmed | Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer |
| title_short | Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer |
| title_sort | short term load forecasting based on multi frequency sequence feature analysis and multi point modified fedformer |
| topic | short-term load forecasting FEDformer VMD time series forecasting lightgbm multi-point modify |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1524319/full |
| work_keys_str_mv | AT kaiyuanhou shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer AT xiaotianzhang shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer AT junjieyang shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer AT jiyunhu shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer AT guangzhiyao shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer AT jiannanzhang shorttermloadforecastingbasedonmultifrequencysequencefeatureanalysisandmultipointmodifiedfedformer |