Kai Hu , Chengchu Yan , Jing Ye , Yizhe Xu , Zhenying Zhu , Yanfeng Gong
{"title":"Sensor fault diagnosis and calibration techniques in building energy systems: A review and future outlook","authors":"Kai Hu , Chengchu Yan , Jing Ye , Yizhe Xu , Zhenying Zhu , Yanfeng Gong","doi":"10.1016/j.buildenv.2024.112365","DOIUrl":null,"url":null,"abstract":"<div><div>While extensive research has been conducted on fault detection and diagnosis (FDD) and sensor calibration in building energy systems, a comprehensive overview of the technical developments in these areas, particularly in response to emerging needs such as high-precision measurement and the advent of new technologies like big data and artificial intelligence, remains limited. To bridge this gap, this paper presents a systematic review of sensor-related topics in building energy systems. It logically summarizes existing research, draws conclusions on current developments, and predicts future trends in sensor technology. The paper categorizes the impacts of sensor failures on energy systems into three primary areas: energy efficiency, thermal fault diagnosis, and indoor thermal comfort. It then outlines the evolution of FDD methods by introducing various models. Finally, recent studies and applications of “true value”/benchmark value determination methods, calibration algorithms, and measurement performance evaluation are presented, along with a summary of the main challenges in sensor calibration and evaluation for ensuring efficient building energy system operation in the context of these emerging trends.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112365"},"PeriodicalIF":7.1000,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360132324012071","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
While extensive research has been conducted on fault detection and diagnosis (FDD) and sensor calibration in building energy systems, a comprehensive overview of the technical developments in these areas, particularly in response to emerging needs such as high-precision measurement and the advent of new technologies like big data and artificial intelligence, remains limited. To bridge this gap, this paper presents a systematic review of sensor-related topics in building energy systems. It logically summarizes existing research, draws conclusions on current developments, and predicts future trends in sensor technology. The paper categorizes the impacts of sensor failures on energy systems into three primary areas: energy efficiency, thermal fault diagnosis, and indoor thermal comfort. It then outlines the evolution of FDD methods by introducing various models. Finally, recent studies and applications of “true value”/benchmark value determination methods, calibration algorithms, and measurement performance evaluation are presented, along with a summary of the main challenges in sensor calibration and evaluation for ensuring efficient building energy system operation in the context of these emerging trends.
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.