Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network

Long Ma, Ling Huang and Huifeng Shi

Energy, 2023, vol. 285, issue C

Abstract: Providing reliable and accurate wind speed predictions for the power system ensures a stable power output. However, the high wind speed uncertainty and the complex spatial–temporal correlation between wind turbines still make wind speed prediction a significant challenge. This paper proposes a novel dynamic spatial–temporal graph network (DSTGN) to make wind speed predictions by accurately capturing the dynamic dependencies of multi-turbine on an arbitrary graph structure. DSTGN adopts an extensible and serialized structure consisting of stacked dynamic spatial–temporal blocks to model dependencies. In spatial dependency extraction, a graph generation module is used in the spatial block to produce static and dynamic spatial information. A parameterized adjacency matrix expands the spatial hypothesis space rather than being limited by the distance between turbines. In temporal dependency extraction, the multi-head attention mechanism models temporal associations of diverse time horizons. Based on publicly available wind datasets, extensive experimental results showed that the proposed model significantly improves performance. Compared to recent state-of-the-art models, MAE, RMSE and MAPE are improved over 4.57%, 4.25% and 8.31%, respectively. Furthermore, through ablation studies, we verified the benefits of each component of the proposed model, especially the positive role of graph sampling in improving performance.

Keywords: Wind speed forecasting; Spatial–temporal dependency; Graph sampling; Dynamic graph convolution (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:285:y:2023:i:c:s0360544223029304

DOI: 10.1016/j.energy.2023.129536

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