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| Short-term Load Forecasting Considering VMD Residuals and Optimizing BiLSTM |
| XIE Yuxuan, WANG Hongjun, YUE Youjun, ZHAO Hui
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| School of Automation Tianjin Complex Control Theory and Application of Key Laboratory, Tianjin University of Technology, Tianjin 300384, China |
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Abstract This study proposes a new method to improve short-term load forecasting accuracy. The method is based on Variational Modal Decomposition (VMD) with consideration of VMD residuals and an Improved Northern Eagle Algorithm (INGO) optimized Bi-directional Long Short Term Memory (BiLSTM) network. The VMD is used to decompose historical load data into multiple eigenmode components (IMFs) and a residual quantity. The BiLSTM model is then constructed separately for each IMF and residual, as well as the associated meteorological parameters. To avoid the impact of poorly selected hyperparameters on prediction accuracy, the INGO algorithm optimizes the implied layer nodes, training times, and learning rates of the BiLSTM. Last but not least, the prediction results are superimposed to obtain the final results. By analyzing specific cases, this paper′s method has demonstrated a higher prediction precision when compared to alternative methods. This validation confirms the effectiveness of the method presented in this article.
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Received: 01 June 2023
Published: 03 January 2025
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