Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU
Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/18/6625 |
| _version_ | 1797580376167677952 |
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| author | Zhuoqun Zou Jing Wang Ning E Can Zhang Zhaocai Wang Enyu Jiang |
| author_facet | Zhuoqun Zou Jing Wang Ning E Can Zhang Zhaocai Wang Enyu Jiang |
| author_sort | Zhuoqun Zou |
| collection | DOAJ |
| description | Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R<sup>2</sup> reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. |
| first_indexed | 2024-03-10T22:49:03Z |
| format | Article |
| id | doaj.art-3e4c9ca3e3004382b8f10e0bebe645df |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:49:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3e4c9ca3e3004382b8f10e0bebe645df2023-11-19T10:27:49ZengMDPI AGEnergies1996-10732023-09-011618662510.3390/en16186625Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRUZhuoqun Zou0Jing Wang1Ning E2Can Zhang3Zhaocai Wang4Enyu Jiang5College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaElectric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, ChinaAccurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper’s model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R<sup>2</sup> reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting.https://www.mdpi.com/1996-1073/16/18/6625short-term load forecastingpower systemsvariational mode decompositionTCNBiGRU |
| spellingShingle | Zhuoqun Zou Jing Wang Ning E Can Zhang Zhaocai Wang Enyu Jiang Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU Energies short-term load forecasting power systems variational mode decomposition TCN BiGRU |
| title | Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU |
| title_full | Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU |
| title_fullStr | Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU |
| title_full_unstemmed | Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU |
| title_short | Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU |
| title_sort | short term power load forecasting an integrated approach utilizing variational mode decomposition and tcn bigru |
| topic | short-term load forecasting power systems variational mode decomposition TCN BiGRU |
| url | https://www.mdpi.com/1996-1073/16/18/6625 |
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