FBiLSTM-Attention short-term load forecasting based on fuzzy logic
Aiming at the problem of high uncertainty in power load data due to various factors, a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting. Firstly, the...
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Hebei University of Science and Technology
2025-02-01
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| Series: | Journal of Hebei University of Science and Technology |
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| Online Access: | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202501005?st=article_issue |
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| _version_ | 1841525886284726272 |
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| author | Yan ZHANG Zepeng KANG Xiaozhi GAO Nan YANG Zhaolei WANG |
| author_facet | Yan ZHANG Zepeng KANG Xiaozhi GAO Nan YANG Zhaolei WANG |
| author_sort | Yan ZHANG |
| collection | DOAJ |
| description | Aiming at the problem of high uncertainty in power load data due to various factors, a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting. Firstly, the raw data, including filling in missing values, conducting correlation analysis and normalizing the data, was preprocessed. Secondly, K-Means clustering was used to transform the data of each feature into fuzzy rules and introduce fuzzy logic processing. In terms of model structure, a bi-directional long short-term memory (BiLSTM) and attention mechanism (Attention) were adopted. Finally, the prediction results of the proposed method with traditional LSTM and BiLSTM Attention models were compared. The results show that the model combined with fuzzy logic has significantly improved accuracy and robustness, and has better predictive performance. The proposed model can effectively improve the ability to handle uncertain data, providing reference for load forecasting study. |
| format | Article |
| id | doaj-art-da0eeed77eda4ac48e6e8b4d0fabc8aa |
| institution | Kabale University |
| issn | 1008-1542 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj-art-da0eeed77eda4ac48e6e8b4d0fabc8aa2025-01-17T06:48:06ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422025-02-01461414810.7535/hbkd.2025yx01005b202501005FBiLSTM-Attention short-term load forecasting based on fuzzy logicYan ZHANG0Zepeng KANG1Xiaozhi GAO2Nan YANG3Zhaolei WANG4School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, ChinaSchool of Electrical Engineering and New Energy, Three Gorges University, Yichang, Hubei 443002, ChinaUltra High Voltage Branch, State Grid Hebei Electric Power Company Limited, Shijiazhuang, Hebei 050070, ChinaAiming at the problem of high uncertainty in power load data due to various factors, a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting. Firstly, the raw data, including filling in missing values, conducting correlation analysis and normalizing the data, was preprocessed. Secondly, K-Means clustering was used to transform the data of each feature into fuzzy rules and introduce fuzzy logic processing. In terms of model structure, a bi-directional long short-term memory (BiLSTM) and attention mechanism (Attention) were adopted. Finally, the prediction results of the proposed method with traditional LSTM and BiLSTM Attention models were compared. The results show that the model combined with fuzzy logic has significantly improved accuracy and robustness, and has better predictive performance. The proposed model can effectively improve the ability to handle uncertain data, providing reference for load forecasting study.https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202501005?st=article_issuedata processing; fuzzy logic; load forecasting; bi-directional long short-term memory(bilstm); attention mechanism(attention) |
| spellingShingle | Yan ZHANG Zepeng KANG Xiaozhi GAO Nan YANG Zhaolei WANG FBiLSTM-Attention short-term load forecasting based on fuzzy logic Journal of Hebei University of Science and Technology data processing; fuzzy logic; load forecasting; bi-directional long short-term memory(bilstm); attention mechanism(attention) |
| title | FBiLSTM-Attention short-term load forecasting based on fuzzy logic |
| title_full | FBiLSTM-Attention short-term load forecasting based on fuzzy logic |
| title_fullStr | FBiLSTM-Attention short-term load forecasting based on fuzzy logic |
| title_full_unstemmed | FBiLSTM-Attention short-term load forecasting based on fuzzy logic |
| title_short | FBiLSTM-Attention short-term load forecasting based on fuzzy logic |
| title_sort | fbilstm attention short term load forecasting based on fuzzy logic |
| topic | data processing; fuzzy logic; load forecasting; bi-directional long short-term memory(bilstm); attention mechanism(attention) |
| url | https://xuebao.hebust.edu.cn/hbkjdx/article/pdf/b202501005?st=article_issue |
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