Toshihiro Mega, Masatada Kawatsu, Y. Fujiwara, Noriyuki
{"title":"各区域电力需求特性识别及节能控制选择方法","authors":"Toshihiro Mega, Masatada Kawatsu, Y. Fujiwara, Noriyuki","doi":"10.1109/ICGEA49367.2020.239686","DOIUrl":null,"url":null,"abstract":"Demand response (DR), which aims to stabilize power supply and cost of electricity, has garnered considerable research interest in recent years. It is expected to be particularly useful in small and medium-sized office buildings, which are responsible for a large share of the total electricity consumption of an area. In this paper, we propose a method for identification of power demand characteristics for each unit, floor, and area based on a power consumption prediction model developed using heterogeneous mixture learning technology. With experimental data obtained from an eight- story office building, we develop an energy-saving control selection method for DR based on the identified power demand characteristics and our evaluation results are reported herein.","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Identification of Power Demand Characteristics for Each Area and Energy Saving Control Selection Method\",\"authors\":\"Toshihiro Mega, Masatada Kawatsu, Y. Fujiwara, Noriyuki\",\"doi\":\"10.1109/ICGEA49367.2020.239686\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Demand response (DR), which aims to stabilize power supply and cost of electricity, has garnered considerable research interest in recent years. It is expected to be particularly useful in small and medium-sized office buildings, which are responsible for a large share of the total electricity consumption of an area. In this paper, we propose a method for identification of power demand characteristics for each unit, floor, and area based on a power consumption prediction model developed using heterogeneous mixture learning technology. With experimental data obtained from an eight- story office building, we develop an energy-saving control selection method for DR based on the identified power demand characteristics and our evaluation results are reported herein.\",\"PeriodicalId\":140641,\"journal\":{\"name\":\"2020 4th International Conference on Green Energy and Applications (ICGEA)\",\"volume\":\"140 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th International Conference on Green Energy and Applications (ICGEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICGEA49367.2020.239686\",\"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 4th International Conference on Green Energy and Applications (ICGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGEA49367.2020.239686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Power Demand Characteristics for Each Area and Energy Saving Control Selection Method
Demand response (DR), which aims to stabilize power supply and cost of electricity, has garnered considerable research interest in recent years. It is expected to be particularly useful in small and medium-sized office buildings, which are responsible for a large share of the total electricity consumption of an area. In this paper, we propose a method for identification of power demand characteristics for each unit, floor, and area based on a power consumption prediction model developed using heterogeneous mixture learning technology. With experimental data obtained from an eight- story office building, we develop an energy-saving control selection method for DR based on the identified power demand characteristics and our evaluation results are reported herein.