M. Caliano, A. Buonanno, G. Graditi, A. Pontecorvo, Gianluca Sforza, M. Valenti
{"title":"纳米电网中单个家庭仅基于消费的负荷预测:一个案例研究","authors":"M. Caliano, A. Buonanno, G. Graditi, A. Pontecorvo, Gianluca Sforza, M. Valenti","doi":"10.23919/AEIT50178.2020.9241127","DOIUrl":null,"url":null,"abstract":"Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.","PeriodicalId":6689,"journal":{"name":"2020 AEIT International Annual Conference (AEIT)","volume":"48 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Consumption based-only load forecasting for individual households in nanogrids: a case study\",\"authors\":\"M. Caliano, A. Buonanno, G. Graditi, A. Pontecorvo, Gianluca Sforza, M. Valenti\",\"doi\":\"10.23919/AEIT50178.2020.9241127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.\",\"PeriodicalId\":6689,\"journal\":{\"name\":\"2020 AEIT International Annual Conference (AEIT)\",\"volume\":\"48 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 AEIT International Annual Conference (AEIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/AEIT50178.2020.9241127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 AEIT International Annual Conference (AEIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/AEIT50178.2020.9241127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Consumption based-only load forecasting for individual households in nanogrids: a case study
Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.