STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting

• URL: http://arxiv.org/abs/2412.11393v1
• Date: Mon, 16 Dec 2024 02:43:29 GMT
• Title: STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
• Authors: Xiaochong Dong, Xuemin Zhang, Ming Yang, Shengwei Mei,
• Abstract summary: We present a dynamic hypergraph learning (STDHL) model to represent spatial features among wind farms. STDHL model incorporates novel dynamic hypergraph convolutional layer to model dynamic spatial correlations and grouped temporal convolutional layer for channel-independent temporal modeling.
• Score: 5.003934238878358
• License:
• Abstract: Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significant modeling challenges. To address this, we propose a spatio-temporal dynamic hypergraph learning (STDHL) model. This model uses a hypergraph structure to represent spatial features among wind farms. Unlike traditional graph structures, which only capture pair-wise node features, hypergraphs create hyperedges connecting multiple nodes, enabling the representation and transmission of higher-order spatial features. The STDHL model incorporates a novel dynamic hypergraph convolutional layer to model dynamic spatial correlations and a grouped temporal convolutional layer for channel-independent temporal modeling. The model uses spatio-temporal encoders to extract features from multi-source covariates, which are mapped to quantile results through a forecast decoder. Experimental results using the GEFCom dataset show that the STDHL model outperforms existing state-of-the-art methods. Furthermore, an in-depth analysis highlights the critical role of spatio-temporal covariates in improving ultra-short-term forecasting accuracy.

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