A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting

Jing Wan, Jiehui Huang, Zhiyuan Liao, Chunquan Li and Peter X. Liu
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Jing Wan: The School of Qianhu, Nanchang University, Nanchang 330031, China
Jiehui Huang: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Zhiyuan Liao: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Chunquan Li: The School of Information Engineering, Nanchang University, Nanchang 330031, China
Peter X. Liu: The School of Information Engineering, Nanchang University, Nanchang 330031, China

Mathematics, 2022, vol. 10, issue 11, 1-20

Abstract: Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.

Keywords: renewable energy; wind power forecasting; hybrid model; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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