A market study of early adopters of fault detection and diagnosis tools for rooftop HVAC systems

被引:2
|
作者
Albayati, Mohammed G. [1 ]
De Oliveira, Julia [1 ]
Patil, Prathamesh [1 ]
Gorthala, Ravi [1 ]
Thompson, Amy E. [1 ]
机构
[1] Univ Connecticut, Unit 5183, Dept Mech Engn, 159 Discovery Dr, Storrs, CT 06269 USA
基金
美国能源部;
关键词
Heating ventilation and air-conditioning; Fault detection and diagnostics; Energy efficiency; Market study; Technology adoption; Rooftop unit; TECHNOLOGY; ACCEPTANCE;
D O I
10.1016/j.egyr.2022.11.017
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Retrofit fault detection and diagnosis (FDD) products are undergoing major advances in their ability to optimize the operation and maintenance of building heating, ventilation, and air conditioning (HVAC) systems as a result of advances in artificial intelligence, cloud computing, and low-cost sensor networks. However, many market barriers still exist to their widespread adoption. This paper is the first to study the market and barriers for FDD products from the perspective of early adopters of HVAC rooftop retrofit FDD products as part of a field study of early adopters. Researchers engaged multiple sites and installed different FDD products at these sites for the purposes of (1) evaluating market readiness, market barriers, and user experience, (2) evaluating energy and demand savings, (3) evaluating the purchase, installation, training, and use process, and (4) determining overall FDD product effectiveness. This paper presents and focuses on the market study goals, methods, results, and findings determined based on survey data collected from key stakeholders participating in the study. These stakeholders included FDD providers, energy efficiency program managers and engineers, HVAC contractors, building owners, facility managers, energy managers, and consultants. The study provides results and analyses concerning (1) current knowledge and awareness levels for FDD technologies, (2) attitudes towards FDD products, (3) market barriers and resources available for FDD, and (4) insights on how stakeholders perceive and determine the value and cost for purchasing, using, and adopting FDD technologies and products. The study produced twelve (12) key findings, which provide valuable input for energy efficiency programs in their development of more effective FDD marketing programs that can increase adoption of FDD technologies and reduce barriers to widespread adoption. (c) 2022 University of Connecticut. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:14915 / 14933
页数:19
相关论文
共 50 条
  • [31] AUTOMATED FAULT DETECTION AND DIAGNOSIS FOR HVAC&R SYSTEMS: FUNCTIONAL DESCRIPTION AND LESSONS LEARNT
    Reddy, T. Agami
    ES2008: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY - 2008, VOL 1, 2009, : 589 - 599
  • [32] A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults
    Wang, Shengwei
    Zhou, Qiang
    Xiao, Fu
    ENERGY AND BUILDINGS, 2010, 42 (04) : 477 - 490
  • [33] Fault Detection and Diagnosis of HVAC System Based on Federated Learning
    Wang, Xiansheng
    Yan, Ke
    2022 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH), 2022, : 501 - 508
  • [34] A comprehensive review: Fault detection, diagnostics, prognostics, and fault modeling in HVAC systems
    Singh, Vijay
    Mathur, Jyotirmay
    Bhatia, Aviruch
    INTERNATIONAL JOURNAL OF REFRIGERATION, 2022, 144 : 283 - 295
  • [35] A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems
    Movahed, Paria
    Taheri, Saman
    Razban, Ali
    APPLIED ENERGY, 2023, 339
  • [36] Cloud-based Parallel Implementation of an Intelligent Classification Algorithm for Fault Detection and Diagnosis of HVAC Systems
    Chang, Long
    Wang, Hong
    Wang, Lingfeng
    2017 INTERNATIONAL SMART CITIES CONFERENCE (ISC2), 2017,
  • [37] A top-down strategy with temporal and spatial partition for fault detection and diagnosis of building HVAC systems
    Wu, Siyu
    Sun, J. Q.
    ENERGY AND BUILDINGS, 2011, 43 (09) : 2134 - 2139
  • [38] Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis
    Du, Zhimin
    Fan, Bo
    Jin, Xinqiao
    Chi, Jinlei
    BUILDING AND ENVIRONMENT, 2014, 73 : 1 - 11
  • [39] An Intelligent Fault Detection Framework for HVAC Systems with Alert Generation
    Sinha A.
    Pandaw A.S.
    Das D.
    SN Computer Science, 4 (5)
  • [40] A LITERATURE REVIEW OF AUTOMATED FAULT DETECTION AND DIAGNOSTICS FOR HVAC SYSTEMS
    Allen-Magande, Hugh
    Khazaii, Javad
    Esmaeili, Amin
    PROCEEDINGS OF ASME 2023 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2023, VOL 7, 2023,