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| Short-term Multi-feature Load Forecasting Using Sample Entropy and BWO in VMD-DELM |
| MA Xinghea, XÜ Leia, MA Yongqiangb
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| a. School of Electrical Engineering and Automation; b. School of Software, Henan Polytechnic University, Jiaozuo 454000, China |
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Abstract To enhance load forecasting accuracy, a short-term load forecasting model incorporating Beluga Whale Optimization (BWO), Sample Entropy (SE), Variational Mode Decomposition (VMD), and Deep Extreme Learning Machine (DELM) is proposed. Initially, based on the principle of minimizing the local sample entropy of each component, using the BWO optimization algorithm to iteratively optimize the mode decomposition number and penalty factor of VMD, thereby decomposes the power load sequence into high-precision sub-sequences. Subsequently, a DELM load forecasting model is constructed for the decomposed load sequences, with initial weights and thresholds optimized using BWO. Feature selection and extraction are then performed using the Pearson coefficient method on input features. Finally, experimental validation is conducted using real load data from a specific location in Australia. The experimental results demonstrate a reduction of the mean absolute percentage error (MAPE) to 16.9% for the load forecasting model. Comparative analysis against mainstream forecasting models confirms its superior accuracy, validating its effectiveness.
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Received: 23 April 2024
Published: 19 May 2026
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