Missing Value Estimation of Energy Consumption of Multi-Unit Air Conditioners using Artificial Neural Networks

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":"44 1","pages":"1-5"},"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.
基于人工神经网络的多机组空调能耗缺失值估计
本文提出了一种利用人工神经网络(ANN)检索多机组空调耗电量缺失数据的方法。丢失数据的问题可能发生在传感器、微控制器或通信问题上。我们必须检索丢失的数据,以便我们可以利用它们找到提高建筑物能源使用效率的解决方案。该方法将其他空调的行为、室内外不同温度、空调垫控制设定值等相关数据与缺失数据相结合,馈送给人工神经网络模型。通过与其他先进分类算法的比较,评价了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
7395
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信