Paradorn Pimporn, S. Kittipiyakul, J. Kudtongngam, H. Fujita
{"title":"Missing Value Estimation of Energy Consumption of Multi-Unit Air Conditioners using Artificial Neural Networks","authors":"Paradorn Pimporn, S. Kittipiyakul, J. Kudtongngam, H. Fujita","doi":"10.1109/ICESIT-ICICTES.2018.8442059","DOIUrl":null,"url":null,"abstract":"This paper proposes a method to retrieve the missing data of power consumption of multi-unit air conditioners by using Artificial Neural Networks (ANN). The problem of missing data may occur from a sensor, a microcontroller or a communication problem. We have to retrieve the missing data in order that we can use them to find a solution to improve the efficiency of energy usage in a building. The proposed method uses related data with the missing data i.e. behavior of other air conditioners, a different temperature among inside, outside, and air conditioner pad controls setting value to feed the ANN model. Effectiveness of the proposed method is evaluated by comparison with other state of art classification algorithms.","PeriodicalId":57136,"journal":{"name":"单片机与嵌入式系统应用","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"单片机与嵌入式系统应用","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICESIT-ICICTES.2018.8442059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a method to retrieve the missing data of power consumption of multi-unit air conditioners by using Artificial Neural Networks (ANN). The problem of missing data may occur from a sensor, a microcontroller or a communication problem. We have to retrieve the missing data in order that we can use them to find a solution to improve the efficiency of energy usage in a building. The proposed method uses related data with the missing data i.e. behavior of other air conditioners, a different temperature among inside, outside, and air conditioner pad controls setting value to feed the ANN model. Effectiveness of the proposed method is evaluated by comparison with other state of art classification algorithms.