Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks

Jinhao Shi, Bo Wang, Kaiyi Luo, Yifei Wu, Min Zhou and Junzo Watada

Energy, 2023, vol. 272, issue C

Abstract: Wind power forecast has played a significant role in modern power systems operation. Meanwhile, interval forecast, as a practical way to represent wind power uncertainty, has attracted considerable attention. In this paper, we propose a novel wind power interval forecast method for multiple wind farms in near regions based on machine learning techniques. First, existing interval forecast methods mainly utilize meta-heuristic algorithms to train the networks, which however, suffer from heavy computation burden and local convergence problem. To remediate this problem, a interval forecast method called Generative Critic Networks (GCN) is proposed, which applies gradient descent algorithm in the parameters optimization and further improve the forecasting performance by a function approximation. Second, considering the spatial correlation of neighboring wind farms, the prediction of these outputs can be regarded as related tasks, thus Multi-Task Learning (MTL) is used as a base to achieve a joint interval forecast of multiple wind farms. Therefore, a unified deep learning model, Multi-Task GCN (MTGCN), is formed to achieve high-quality PIs of multiple wind farms. Finally, experimental results on different datasets show that the proposed algorithm can obtain high-quality prediction interval than other methods, leading to a reduction of at least 9.5% in the interval width.

Keywords: Wind power; Interval forecast; Lower and upper bound estimation; Multi-task learning; Generative critic networks (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:272:y:2023:i:c:s0360544223005108

DOI: 10.1016/j.energy.2023.127116

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