{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:07:07Z","timestamp":1735016827882,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Power system load forecasting is the foundation of power system operation and planning. Meteorological factors such as temperature, humidity, and wind speed have a significant impact on the load of the power system. In recent years, with the popularity of data-driven methods, power system load forecasting has developed. However, in existing research on power systems, the study of relationships between variables is predominantly based on correlation, with little mention of the concept of causality. In this paper, an adaptive time window strategy is proposed to identify time periods where causal relationships may change. Then, the Time Causal Discovery Framework (TCDF) was used at each time window to explore the causal relationship between electricity load and climate data. Finally, compared with the traditional correlation-based analysis method, the experiment shows that our method not only explains the causal relationship between power load and climate, but also effectively improves the prediction performance.<\/jats:p>","DOI":"10.3233\/faia241422","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:40Z","timestamp":1734947320000},"source":"Crossref","is-referenced-by-count":0,"title":["Electric Power Load Forecasting: Discovering Causal Links with Meteorological Data"],"prefix":"10.3233","author":[{"given":"Hongwei","family":"Ma","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Huimin","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks, Nari Technology Co., Ltd., Nanjing, Jiangsu, 211106, China"}]},{"given":"Weijie","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:40Z","timestamp":1734947320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241422","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}