A multi-factor clustering integration paradigm for wind speed point-interval prediction based on feature selection and optimized inverted transformer

Jujie Wang, Weiyi Jiang, Shuqin Shu and Xuecheng He

Energy, 2025, vol. 320, issue C

Abstract: Accurate wind speed prediction is essential for enhancing wind power integration and ensuring grid stability. These limitations include insufficient consideration of external factors, oversimplified handling of temporal correlations. This paper proposes a multi-factor clustering integration model for wind speed point-interval prediction, incorporating advanced feature selection and an optimized inverted Transformer. The approach begins with a characteristic contribution assessment method to identify critical factors impacting prediction accuracy, ensuring the model leverages the most influential features. Subsequently, through an intrinsic feature extraction method, the wind speed series are further segmented into multiple clusters, capturing multi-scale dependencies and complex temporal patterns that may be overlooked by traditional models. This segmented approach enables a dual-phase forecasting framework, where the optimized inverted Transformer is applied to each cluster, increasing both predictive stability and precision by aligning forecasts with specific data patterns. Additionally, a point to interval prediction mechanism generates probabilistic intervals that effectively capture the uncertainty inherent in wind speed data. Experiments conducted on two datasets confirm the model's superiority, achieving the lowest mean squared error among comparison models. This integrated methodology enhances the accuracy, robustness, and interpretability of short-term wind speed forecasts, providing a comprehensive solution to the inherent challenges of wind speed prediction.

Keywords: Wind speed point-interval prediction; Extrinsic factor selection; Intrinsic feature extraction; Optimized invert transformer model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225008527
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:320:y:2025:i:c:s0360544225008527

DOI: 10.1016/j.energy.2025.135210

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().