{"title":"节假日期间建筑能耗预测","authors":"Qingyao Qiao, A. Yunusa‐Kaltungo, R. Edwards","doi":"10.1109/PowerAfrica52236.2021.9543455","DOIUrl":null,"url":null,"abstract":"Predicting sudden changes in energy consumption within a short time period remains a challenging task for long-term building energy consumption Prediction. In order to better predict long-term energy consumption during holiday periods, this paper proposes a novel Prophet model to adequately capture the energy usage patterns of a classroom room building during Christmas periods under several data scenarios. The results showed that the incorporation of additional weather information as often advocated by several earlier studies failed to improve the prediction accuracy. Although the extension of the training data size can significantly improve the prediction outcomes under certain scenarios, it failed to capture the sudden drop in energy consumption when holiday effects were incorporated. The best performance was achieved when the model was fed with 2-year training data as well as integrating holiday effects.","PeriodicalId":370999,"journal":{"name":"2021 IEEE PES/IAS PowerAfrica","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Predicting building energy consumption during holiday periods\",\"authors\":\"Qingyao Qiao, A. Yunusa‐Kaltungo, R. Edwards\",\"doi\":\"10.1109/PowerAfrica52236.2021.9543455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting sudden changes in energy consumption within a short time period remains a challenging task for long-term building energy consumption Prediction. In order to better predict long-term energy consumption during holiday periods, this paper proposes a novel Prophet model to adequately capture the energy usage patterns of a classroom room building during Christmas periods under several data scenarios. The results showed that the incorporation of additional weather information as often advocated by several earlier studies failed to improve the prediction accuracy. Although the extension of the training data size can significantly improve the prediction outcomes under certain scenarios, it failed to capture the sudden drop in energy consumption when holiday effects were incorporated. The best performance was achieved when the model was fed with 2-year training data as well as integrating holiday effects.\",\"PeriodicalId\":370999,\"journal\":{\"name\":\"2021 IEEE PES/IAS PowerAfrica\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE PES/IAS PowerAfrica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PowerAfrica52236.2021.9543455\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE PES/IAS PowerAfrica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PowerAfrica52236.2021.9543455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting building energy consumption during holiday periods
Predicting sudden changes in energy consumption within a short time period remains a challenging task for long-term building energy consumption Prediction. In order to better predict long-term energy consumption during holiday periods, this paper proposes a novel Prophet model to adequately capture the energy usage patterns of a classroom room building during Christmas periods under several data scenarios. The results showed that the incorporation of additional weather information as often advocated by several earlier studies failed to improve the prediction accuracy. Although the extension of the training data size can significantly improve the prediction outcomes under certain scenarios, it failed to capture the sudden drop in energy consumption when holiday effects were incorporated. The best performance was achieved when the model was fed with 2-year training data as well as integrating holiday effects.