{"type":"ScholarlyArticle","title":"Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer","authors":[{"name":"Yang Zhang","slug":"yang-zhang","given":"Yang","family":"Zhang","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"},{"name":"Lingbo Liu","slug":"lingbo-liu","given":"Lingbo","family":"Liu","affiliation":"Department Land Surveying and Geo-Informatics , The Hong Kong Polytechnic University , Hong Kong; Smart Cities Research Institute , The Hong Kong Polytechnic University , Hong Kong"},{"name":"Xinyu Xiong","slug":"xinyu-xiong","given":"Xinyu","family":"Xiong","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"},{"name":"Guanbin Li","slug":"guanbin-li","given":"Guanbin","family":"Li","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Research Institute , Sun Yat-sen University , Shenzhen , China"},{"name":"Guoli Wang","slug":"guoli-wang","given":"Guoli","family":"Wang","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"},{"name":"Liang Lin","slug":"liang-lin","given":"Liang","family":"Lin","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"}],"abstract":"Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. However, safely and stably integrating the high permeability intermittent power energy into electric power systems remains challenging. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation. In this work, we propose a novel end-to-end wind power forecasting model named Hierarchical Spatial-Temporal Transformer Network (HSTTN) to address the long-term WPF problems. Specifically, we construct an hourglass-shaped encoder-decoder framework with skip-connections to jointly model representations aggregated in hierarchical temporal scales, which benefits long-term forecasting. Based on this framework, we capture the inter-scale long-range temporal dependencies and global spatial correlations with two parallel Transformer skeletons and strengthen the intra-scale connections with downsampling and upsampling operations. Moreover, the complementary information from spatial and temporal features is fused and propagated in each other via Contextual Fusion Blocks (CFBs) to promote the prediction further. Extensive experimental results on two large-scale real-world datasets demonstrate the superior performance of our HSTTN over existing solutions.","doi":"10.24963/ijcai.2023/700","url":"https://ijcai.org/proceedings/2023/700","pdf":"https://ijcai.org/proceedings/2023/0700.pdf","markdown":"https://ijcai.org/proceedings/2023/700.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/700","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":700,"pages":{"first":6308,"last":6316},"keywords":["AI for Good: Data Mining","AI for Good: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmad and Zhang, 2022] Tanveer Ahmad and Dongdong Zhang. 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