{"title":"基于 NeuralProphet 和 Bi-LSTM-SA 的组合模型电力负荷预测","authors":"Dongpeng Zhao, Shouzhi Xu, Haowen Sun, Bitao Li, Mengying Jiang, Shiyu Tan","doi":"10.1088/1742-6596/2781/1/012025","DOIUrl":null,"url":null,"abstract":"This study proposes an innovative method for forecasting electricity load that combines NeuralProphet’s time series analysis capability with Bi-LSTM-SA’s self-attention mechanism. The method improves prediction accuracy, reliability, and interpretability by analyzing trends, cycles, and holiday impacts, as well as considering climatic factors as key external variables. A peak interval weighted mean square error indicator is introduced to optimize the weights in the model combination strategy. This improves the prediction accuracy during peak times, making this method superior to any single sub-model in terms of prediction performance.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combined model electricity load forecasting based on NeuralProphet and Bi-LSTM-SA\",\"authors\":\"Dongpeng Zhao, Shouzhi Xu, Haowen Sun, Bitao Li, Mengying Jiang, Shiyu Tan\",\"doi\":\"10.1088/1742-6596/2781/1/012025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes an innovative method for forecasting electricity load that combines NeuralProphet’s time series analysis capability with Bi-LSTM-SA’s self-attention mechanism. The method improves prediction accuracy, reliability, and interpretability by analyzing trends, cycles, and holiday impacts, as well as considering climatic factors as key external variables. A peak interval weighted mean square error indicator is introduced to optimize the weights in the model combination strategy. This improves the prediction accuracy during peak times, making this method superior to any single sub-model in terms of prediction performance.\",\"PeriodicalId\":16821,\"journal\":{\"name\":\"Journal of Physics: Conference Series\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Physics: Conference Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/1742-6596/2781/1/012025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics: Conference Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1742-6596/2781/1/012025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combined model electricity load forecasting based on NeuralProphet and Bi-LSTM-SA
This study proposes an innovative method for forecasting electricity load that combines NeuralProphet’s time series analysis capability with Bi-LSTM-SA’s self-attention mechanism. The method improves prediction accuracy, reliability, and interpretability by analyzing trends, cycles, and holiday impacts, as well as considering climatic factors as key external variables. A peak interval weighted mean square error indicator is introduced to optimize the weights in the model combination strategy. This improves the prediction accuracy during peak times, making this method superior to any single sub-model in terms of prediction performance.