Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network
العنوان: | Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network |
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المؤلفون: | Changchun Cai, Yuanjia Li, Zhenghua Su, Tianqi Zhu, Yaoyao He |
المصدر: | Applied Sciences; Volume 12; Issue 13; Pages: 6647 |
بيانات النشر: | Multidisciplinary Digital Publishing Institute, 2022. |
سنة النشر: | 2022 |
مصطلحات موضوعية: | Fluid Flow and Transfer Processes, Process Chemistry and Technology, General Engineering, General Materials Science, short-term load forecasting, variational modal decomposition (VMD), gated recurrent unit (GRU), time convolutional network (TCN), hybrid algorithm, Instrumentation, Computer Science Applications |
الوصف: | 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. |
وصف الملف: | application/pdf |
اللغة: | English |
تدمد: | 2076-3417 |
DOI: | 10.3390/app12136647 |
الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90894bb64d571a8f48b515bf5b8036ccTest |
حقوق: | OPEN |
رقم الانضمام: | edsair.doi.dedup.....90894bb64d571a8f48b515bf5b8036cc |
قاعدة البيانات: | OpenAIRE |
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