Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network
With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6647 |
| _version_ | 1797480889960103936 |
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| author | Changchun Cai Yuanjia Li Zhenghua Su Tianqi Zhu Yaoyao He |
| author_facet | Changchun Cai Yuanjia Li Zhenghua Su Tianqi Zhu Yaoyao He |
| author_sort | Changchun Cai |
| collection | DOAJ |
| description | With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost. |
| first_indexed | 2024-03-09T22:06:42Z |
| format | Article |
| id | doaj.art-8812d6d8c7354cdcb8fbc2b56c1c76d6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:42Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8812d6d8c7354cdcb8fbc2b56c1c76d62023-11-23T19:40:21ZengMDPI AGApplied Sciences2076-34172022-06-011213664710.3390/app12136647Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid NetworkChangchun Cai0Yuanjia Li1Zhenghua Su2Tianqi Zhu3Yaoyao He4Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaState Grid Changzhou Power Supply Company, Changzhou 210024, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaJiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, ChinaWith the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost.https://www.mdpi.com/2076-3417/12/13/6647short-term load forecastingvariational modal decomposition (VMD)gated recurrent unit (GRU)time convolutional network (TCN)hybrid algorithm |
| spellingShingle | Changchun Cai Yuanjia Li Zhenghua Su Tianqi Zhu Yaoyao He Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network Applied Sciences short-term load forecasting variational modal decomposition (VMD) gated recurrent unit (GRU) time convolutional network (TCN) hybrid algorithm |
| title | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
| title_full | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
| title_fullStr | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
| title_full_unstemmed | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
| title_short | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
| title_sort | short term electrical load forecasting based on vmd and gru tcn hybrid network |
| topic | short-term load forecasting variational modal decomposition (VMD) gated recurrent unit (GRU) time convolutional network (TCN) hybrid algorithm |
| url | https://www.mdpi.com/2076-3417/12/13/6647 |
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