SPLNet: A sequence-to-one learning network with time-variant structure for regional wind speed prediction

Although regional wind speed prediction is of great value in the development of wind power, it is rarely studied in the past years. To fill this gap, we introduce a deep learning method for multi-step regional wind speed prediction, which aims to model the spatiotemporal dynamics of wind speed in a given region and make predictions across time steps. There are several important challenges for it: (1) how to simultaneously build long-term temporal dependencies and long-range spatial interactions of wind speed; (2) how to capture the fluctuant, intermittent, and chaotic nature of wind speed. In this paper, we propose a novel framework named Sequence-to-one Predictive Learning Net (SPLNet) for multi-step regional wind speed prediction. To effectively utilize historical information and build the long-term temporal dependencies, SPLNet regards multi-step prediction as multi-time sequence-to-one (Seq2one) learning, where each prediction is made based on a previous historical sequence. Besides, a new spatiotemporal dynamics attention (STD-Atten) unit is developed as the key component of SPLNet, which can realize Seq2one learning and capture long-range spatial dependencies. Moreover, to better model the nonstationary and violent nature of wind speed, SPLNet adopts a time-variant structure, where different STD-Atten units are used for predicting wind speed at different time steps. To stabilize model training and enhance prediction performance, a step-separated training procedure is put forward. Comprehensive experiments have been conducted on three real-world datasets, where regional wind speed is illustrated as images. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces a deep learning method for multi-step regional wind speed prediction, aiming to model the spatiotemporal dynamics of wind speed and make predictions across time steps. The proposed method addresses several challenges and achieves promising results in experiments.