Short-Term Wind Power Forecast Based on STL-IAOA-iTransformer Algorithm: A Case Study in Northwest China

Yang, Z, Yang, B, Liu, W, Li, M, Wang, J, Jiang, L ORCID: 0000-0001-6531-2791, Sang, Y and Pan, Z (2025) Short-Term Wind Power Forecast Based on STL-IAOA-iTransformer Algorithm: A Case Study in Northwest China Energy Engineering Journal of the Association of Energy Engineering, 122 (2). pp. 405-430. ISSN 0199-8595, 1546-0118

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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). First, to fully extract the power data features, STL is used to decompose the original data into components with less redundant information. The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast. The final predicted short-term wind power curve is obtained by combining the predicted components. To improve the model accuracy, IAOA is employed to optimize the hyperparameters of iTransformer. The proposed approach is validated using real-generation data from different seasons and different power stations in Northwest China, and ablation experiments have been conducted. Furthermore, to validate the superiority of the proposed approach under different wind characteristics, real power generation data from southwest China are utilized for experiments. The comparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.

Item Type:	Article
Uncontrolled Keywords:	4605 Data Management and Data Science, 46 Information and Computing Sciences, 7 Affordable and Clean Energy
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	03 Jun 2025 07:11
Last Modified:	24 Jan 2026 05:09
DOI:	10.32604/ee.2025.059515
Open Access URL:	https://www.techscience.com/energy/v122n2/59430
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3193048
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