Spatiotemporal wind power forecasting approach based on multi-factor extraction method and an indirect strategy

Shaolong Sun, Zongjuan Du, Kun Jin, Hongtao Li and Shouyang Wang

Applied Energy, 2023, vol. 350, issue C, No S0306261923011133

Abstract: Accurate ultra-short-term wind power forecasting is a prerequisite for decision making related to the management of power systems. Existing approaches used to forecast wind power ignored the correlation between wind power outputs under similar wind power, which provides important information for wind power forecasting. A spatiotemporal approach based on an indirect strategy and a multi-factor extraction model is proposed in this study to achieve more accurate power prediction. Specifically, wind power and other variables are classified into four categories according to wind direction, and multivariate feature extraction and wind power forecasting are performed separately for each category. In the experimental study, taking two real-world datasets of wind farms in Northeast China as examples, the proposed approach is compared with eight benchmarking approaches, and the results demonstrate the effectiveness and robustness of our approach. In addition, we further conduct the DM test, and the sensitivity analysis to discuss the effect of hyperparameters on forecasting performance. The results of DM verify the superiority of the proposed approach in statistic. This study provides a new high-precision approach and a new forecasting strategy for future wind power forecasting.

Keywords: Wind power forecasting; Indirect strategy; Spatiotemporal factor; Factor extraction; Deep learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.apenergy.2023.121749

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