{"type":"ScholarlyArticle","title":"Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks","authors":[{"name":"Weixuan Lin","slug":"weixuan-lin","given":"Weixuan","family":"Lin","affiliation":"McGill University , Montreal , Quebec"},{"name":"Di Wu","slug":"di-wu","given":"Di","family":"Wu","affiliation":"McGill University , Montreal , Quebec"}],"abstract":"An accurate short-term electric load forecasting is critical for modern electric power systems' safe and economical operation. \r\nElectric load forecasting can be formulated as a multi-variate time series problem. Residential houses in the same neighborhood may be affected by similar factors and share some latent spatial dependencies. However, most of the existing works on electric load forecasting fail to explore such dependencies. In recent years, graph neural networks (GNNs) have shown impressive success in modeling such dependencies. However, such GNN based models usually would require a large amount of training data. We may have a minimal amount of data available to train a reliable forecasting model for houses in a new neighborhood area. At the same time, we may have a large amount of historical data collected from other houses that can be leveraged to improve the new neighborhood's prediction performance. In this paper, we propose an attentive transfer learning-based GNN model that can utilize the learned prior knowledge to improve the learning process in a new area.  The transfer process is achieved by an attention network, which generically avoids negative transfer by leveraging knowledge from multiple sources.\r\nExtensive experiments have been conducted on real-world data sets.  Results have shown that the proposed framework can consistently outperform baseline models in different areas.","doi":"10.24963/ijcai.2021/374","url":"https://ijcai.org/proceedings/2021/374","pdf":"https://ijcai.org/proceedings/2021/0374.pdf","markdown":"https://ijcai.org/proceedings/2021/374.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/374","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":374,"pages":{"first":2716,"last":2722},"keywords":["Machine Learning: Time-series; Data Streams","Multidisciplinary Topics and Applications: Computational Sustainability","Multidisciplinary Topics and Applications: Natural Sciences"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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