{"title":"基于时间序列和改进的反向传播神经网络的区域电能替代电位预测","authors":"Ziwen Cai, Yutao Xu, Yong Xiao, Dunhui Chen, Yun Zhao, Zhukui Tan","doi":"10.1088/1742-6596/2636/1/012029","DOIUrl":null,"url":null,"abstract":"Abstract A combined time-series-based regression forecasting and GRA-IPSO-BP structure is proposed to forecast the electricity replacement forecasts using a time series model based on the triple exponential smoothing forecasting method. The forecast results are corrected using the GRA-IPSO-BP structure. The results of the algorithm show that using a combination of time series and GRA-IPSO-BP structure can significantly improve the forecasting accuracy of electricity replacement compared to single-method forecasting.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regional electrical energy substitution potential prediction based on time series and improved back propagation neural network\",\"authors\":\"Ziwen Cai, Yutao Xu, Yong Xiao, Dunhui Chen, Yun Zhao, Zhukui Tan\",\"doi\":\"10.1088/1742-6596/2636/1/012029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A combined time-series-based regression forecasting and GRA-IPSO-BP structure is proposed to forecast the electricity replacement forecasts using a time series model based on the triple exponential smoothing forecasting method. The forecast results are corrected using the GRA-IPSO-BP structure. The results of the algorithm show that using a combination of time series and GRA-IPSO-BP structure can significantly improve the forecasting accuracy of electricity replacement compared to single-method forecasting.\",\"PeriodicalId\":44008,\"journal\":{\"name\":\"Journal of Physics-Photonics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Physics-Photonics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1742-6596/2636/1/012029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics-Photonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1742-6596/2636/1/012029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
Regional electrical energy substitution potential prediction based on time series and improved back propagation neural network
Abstract A combined time-series-based regression forecasting and GRA-IPSO-BP structure is proposed to forecast the electricity replacement forecasts using a time series model based on the triple exponential smoothing forecasting method. The forecast results are corrected using the GRA-IPSO-BP structure. The results of the algorithm show that using a combination of time series and GRA-IPSO-BP structure can significantly improve the forecasting accuracy of electricity replacement compared to single-method forecasting.