WDMNet: Modeling diverse variations of regional wind speed for multi-step predictions.

Abstract

Regional wind speed prediction plays an important role in the development of wind power, which is usually recorded in the form of two orthogonal components, namely U-wind and V-wind. The regional wind speed has the characteristics of diverse variations, which are reflected in three aspects: (1) The spatially diverse variations of regional wind speed indicate that wind speed has different dynamic patterns at different positions; (2) The distinct variations between U-wind and V-wind denote that U-wind and V-wind at the same position exhibit different dynamic patterns; (3) The non-stationary variations of wind speed represent that the intermittent and chaotic nature of wind speed. In this paper, we propose a novel framework named Wind Dynamics Modeling Network (WDMNet) to model the diverse variations of regional wind speed and make accurate multi-step predictions. To jointly capture the spatially diverse variations and the distinct variations between U-wind and V-wind, WDMNet leverages a new neural block called Involution Gated Recurrent Unit Partial Differential Equation (Inv-GRU-PDE) as its key component. The block adopts involution to model spatially diverse variations and separately constructs hidden driven PDEs of U-wind and V-wind. The construction of PDEs in this block is achieved by a new Involution PDE (InvPDE) layers. Besides, a deep data-driven model is also introduced in Inv-GRU-PDE block as the complement to the constructed hidden PDEs for sufficiently modeling regional wind dynamics. Finally, to effectively capture the non-stationary variations of wind speed, WDMNet follows a time-variant structure for multi-step predictions. Comprehensive experiments have been conducted on two real-world datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art techniques.