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Adaptive Bi-Directional LSTM Short-Term Load Forecasting with Improved Attention Mechanisms

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  • Kun Yu

    (College of Electrical Engineering New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to improve load forecasting accuracy. An improved adaptive K-means clustering algorithm is designed for load pattern recognition and avoiding local sub-optimal clustering centroids. We also design bi-directional long-short-term memory neural networks with an attention mechanism to filter important load information and perform load forecasting for each recognized load pattern. The numerical results on the public load dataset show that our proposed method effectively forecasts the residential load with a high accuracy.

Suggested Citation

  • Kun Yu, 2024. "Adaptive Bi-Directional LSTM Short-Term Load Forecasting with Improved Attention Mechanisms," Energies, MDPI, vol. 17(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3709-:d:1444114
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    References listed on IDEAS

    as
    1. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
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