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

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 optimiz...

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Vydané v:Energy Engineering : Journal of the Association of Energy Engineers Ročník 122; číslo 2; s. 405 - 430
Hlavní autori: Yang, Zhaowei, Liu, Wenqi, Li, Miwei, Wang, Jiarong, Yang, Bo, Jiang, Lin, Sang, Yiyan, Pan, Zhenning
Médium: Journal Article Trade Publication Article
Jazyk:English
Vydavateľské údaje: Atlanta Tech Science Press 2025
Predmet:
Ablation
Accuracy
Algorithms
Decomposition
Meteorological data
Optimization
Power plants
Prediction models
Smart grid
Swarm intelligence
Wind power
China
ISSN:1546-0118, 0199-8595, 1546-0118
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Shrnutí: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.
ISSN:1546-0118
0199-8595
1546-0118
DOI:10.32604/ee.2025.059515