STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913613599473664 |
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| author | Dong, Xiaochong Zhang, Xuemin Yang, Ming Mei, Shengwei |
| author_facet | Dong, Xiaochong Zhang, Xuemin Yang, Ming Mei, Shengwei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_11393 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting Dong, Xiaochong Zhang, Xuemin Yang, Ming Mei, Shengwei Machine Learning Signal Processing 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. |
| title | STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2412.11393 |