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Addressing the critical role of short-term load forecasting in power systems and the limitations of conventional methods, this study proposes a novel forecasting model incorporating an Improved Whale Optimization Algorithm (IWOA). The model integrates an adaptive parameter adjustment mechanism with a hybrid optimization strategy to refine Extreme Learning Machine (ELM) parameters, thereby enhancing prediction accuracy. Empirical validation using operational load data demonstrates the superior performance of the proposed algorithm over traditional models in key metrics including Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

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Research on Short-Term Load Forecasting in Power Systems Based on the Improved Whale Optimization Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Jiawu Ma,
  • Haiping Zhang,
  • Wenbin Chen,
  • Xiongyi Yuan,
  • Chunmin Hong,
  • Qinghao Li,
  • Feng Pang,
  • Ting Gao

摘要

Addressing the critical role of short-term load forecasting in power systems and the limitations of conventional methods, this study proposes a novel forecasting model incorporating an Improved Whale Optimization Algorithm (IWOA). The model integrates an adaptive parameter adjustment mechanism with a hybrid optimization strategy to refine Extreme Learning Machine (ELM) parameters, thereby enhancing prediction accuracy. Empirical validation using operational load data demonstrates the superior performance of the proposed algorithm over traditional models in key metrics including Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).