{"type":"ScholarlyArticle","title":"Intelligent Decision Support for Improving Power Management","authors":[{"name":"Yongqing Zheng","slug":"yongqing-zheng","given":"Yongqing","family":"Zheng","affiliation":"School of Software , Shandong University; Dareway Software Co. Ltd"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"School of Computer Science and Engineering , Nanyang Technological University; Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly"},{"name":"Kun Zhang","slug":"kun-zhang","given":"Kun","family":"Zhang","affiliation":"School of Software , Shandong University; Dareway Software Co. Ltd; School of Information Science and Engineering , University of Jinan"},{"name":"Yuliang Shi","slug":"yuliang-shi","given":"Yuliang","family":"Shi","affiliation":"School of Software , Shandong University; Dareway Software Co. Ltd"},{"name":"Cyril Leung","slug":"cyril-leung","given":"Cyril","family":"Leung","affiliation":"Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly; Department of Electrical and Computer Engineering , The University of British Columbia"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"School of Computer Science and Engineering , Nanyang Technological University; Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly"}],"abstract":"With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.","doi":"10.24963/ijcai.2019/965","url":"https://ijcai.org/proceedings/2019/965","pdf":"https://ijcai.org/proceedings/2019/0965.pdf","markdown":"https://ijcai.org/proceedings/2019/965.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/965","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":965,"pages":{"first":6587,"last":6589},"keywords":["Applications: Energy","AI: Uncertainty in AI"],"videos":[],"references":[{"position":1,"raw":"[Hippert et al., 2001] Henrique S. 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