Zhaowei Yang1, Bo Yang2,*, Wenqi Liu1, Miwei Li2, Jiarong Wang2, Lin Jiang3, Yiyan Sang4, Zhenning Pan5
Energy Engineering, Vol.122, No.2, pp. 405-430, 2025, DOI:10.32604/ee.2025.059515
- 31 January 2025
Abstract Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids. Although numerous studies have employed various methods to forecast wind power, there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction. To improve the accuracy of short-term wind power forecast, this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer, which is based on seasonal and trend decomposition using LOESS (STL) and iTransformer model optimized by improved arithmetic optimization algorithm (IAOA).… More >
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