{"title":"A review on building occupancy estimation methods","authors":"Muhammad Tirta Mulia, S. Supangkat, N. Hariyanto","doi":"10.1109/ICTSS.2017.8288878","DOIUrl":null,"url":null,"abstract":"Building occupancy information becomes important in term of indoor environmental quality, energy building simulation as well as energy consumption and energy saving approach. Occupancy estimation provides energy efficiency up to 50%. Occupancy estimation is strongly influenced by the characteristics of the building and the behavior of the occupants. This situation makes accurate occupancy estimation difficult. Several models to resolve the problem are reviewed in this article which includes approaches from the side of the sensing and processing method. Processing approach using statistical methods and machine learning. Previous recent research related to relevant models and applications in occupancy estimation is discussed. Further research prospects are proposed based on analysis of previous work.","PeriodicalId":149604,"journal":{"name":"2017 International Conference on ICT For Smart Society (ICISS)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on ICT For Smart Society (ICISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTSS.2017.8288878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Building occupancy information becomes important in term of indoor environmental quality, energy building simulation as well as energy consumption and energy saving approach. Occupancy estimation provides energy efficiency up to 50%. Occupancy estimation is strongly influenced by the characteristics of the building and the behavior of the occupants. This situation makes accurate occupancy estimation difficult. Several models to resolve the problem are reviewed in this article which includes approaches from the side of the sensing and processing method. Processing approach using statistical methods and machine learning. Previous recent research related to relevant models and applications in occupancy estimation is discussed. Further research prospects are proposed based on analysis of previous work.