Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM
Aiming at the diversity and flexibility of electric loads and their inherent nonlinearity and temporality in the context of the new era. A forecasting method is proposed combining Variational Modal Decomposition (VMD) and EDE-BiLSTM. Initially, the load data are decomposed using VMD to obtain severa...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10815728/ |
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| author | Yibo Lai Qifeng Wang Gang Chen Yu Bai Peiyu Zhao Xiaojing Liao Shuang Wu Changyou Men Quan Sun |
| author_facet | Yibo Lai Qifeng Wang Gang Chen Yu Bai Peiyu Zhao Xiaojing Liao Shuang Wu Changyou Men Quan Sun |
| author_sort | Yibo Lai |
| collection | DOAJ |
| description | Aiming at the diversity and flexibility of electric loads and their inherent nonlinearity and temporality in the context of the new era. A forecasting method is proposed combining Variational Modal Decomposition (VMD) and EDE-BiLSTM. Initially, the load data are decomposed using VMD to obtain several modal components. These components are then integrated with temperature, wind speed, precipitation, electricity price, and holiday characteristics to capture a comprehensive set of influencing factors. Subsequently, the modal components undergo training and learning through the EDE-BiLSTM model, aiming to minimize the test set’s root mean square error (RMSE). Each modal component is modelled individually, and the final prediction is derived by summing the predicted values of all components. The method’s effectiveness is validated using load datasets from Singapore and Australia. The model in this paper outperforms the comparison model with root mean square error (RMSE), mean absolute percentage error (MAPE),and R-squared (R2) for the two datasets respectively. The experimental results show that the proposed method performs better than other methods and can improve the accuracy of short-term power load forecasting. |
| format | Article |
| id | doaj-art-1205be4b191c4932918e5ad8a33e75d5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1205be4b191c4932918e5ad8a33e75d52025-01-24T00:01:34ZengIEEEIEEE Access2169-35362025-01-0113104811048810.1109/ACCESS.2024.352265610815728Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTMYibo Lai0Qifeng Wang1Gang Chen2Yu Bai3https://orcid.org/0000-0001-5562-5102Peiyu Zhao4https://orcid.org/0009-0006-3506-0216Xiaojing Liao5https://orcid.org/0009-0002-0760-2023Shuang Wu6Changyou Men7Quan Sun8State Grid Hangzhou Power Supply Company, Hangzhou, ChinaState Grid Hangzhou Power Supply Company, Hangzhou, ChinaAdvanced Institute of Information Technology, Peking University, Hangzhou, ChinaAdvanced Institute of Information Technology, Peking University, Hangzhou, ChinaAdvanced Institute of Information Technology, Peking University, Hangzhou, ChinaAdvanced Institute of Information Technology, Peking University, Hangzhou, ChinaAdvanced Institute of Information Technology, Peking University, Hangzhou, ChinaHangzhou Vango Technologies Inc., Hangzhou, ChinaHangzhou Vango Technologies Inc., Hangzhou, ChinaAiming at the diversity and flexibility of electric loads and their inherent nonlinearity and temporality in the context of the new era. A forecasting method is proposed combining Variational Modal Decomposition (VMD) and EDE-BiLSTM. Initially, the load data are decomposed using VMD to obtain several modal components. These components are then integrated with temperature, wind speed, precipitation, electricity price, and holiday characteristics to capture a comprehensive set of influencing factors. Subsequently, the modal components undergo training and learning through the EDE-BiLSTM model, aiming to minimize the test set’s root mean square error (RMSE). Each modal component is modelled individually, and the final prediction is derived by summing the predicted values of all components. The method’s effectiveness is validated using load datasets from Singapore and Australia. The model in this paper outperforms the comparison model with root mean square error (RMSE), mean absolute percentage error (MAPE),and R-squared (R2) for the two datasets respectively. The experimental results show that the proposed method performs better than other methods and can improve the accuracy of short-term power load forecasting.https://ieeexplore.ieee.org/document/10815728/Short-term load forecastingvariational modal decomposition (VMD)EDE-BiLSTM |
| spellingShingle | Yibo Lai Qifeng Wang Gang Chen Yu Bai Peiyu Zhao Xiaojing Liao Shuang Wu Changyou Men Quan Sun Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM IEEE Access Short-term load forecasting variational modal decomposition (VMD) EDE-BiLSTM |
| title | Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM |
| title_full | Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM |
| title_fullStr | Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM |
| title_full_unstemmed | Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM |
| title_short | Short-Term Power Load Prediction Method Based on VMD and EDE-BiLSTM |
| title_sort | short term power load prediction method based on vmd and ede bilstm |
| topic | Short-term load forecasting variational modal decomposition (VMD) EDE-BiLSTM |
| url | https://ieeexplore.ieee.org/document/10815728/ |
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