Mobile Crowdsourcing of Data for Fault Detection and Diagnosis in Smart Buildings

被引:18
|
作者
Lazarova-Molnar, Sanja [1 ]
Logason, Halldor Por [1 ]
Andersen, Peter Gronbaek [1 ]
Kjaergaard, Mikkel Baun [1 ]
机构
[1] Univ Southern Denmark, Ctr Energy Informat, Campusvej 55, DK-5230 Odense M, Denmark
来源
2016 RESEARCH IN ADAPTIVE AND CONVERGENT SYSTEMS | 2016年
关键词
Crowdsourcing; energy performance; buildings; fault detection and diagnosis; data collection; occupants;
D O I
10.1145/2987386.2987416
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Energy use of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas contain goals related to reducing the energy consumption and carbon footprint. Timely and accurate fault detection and diagnosis (FDD) in building management systems (BMS) have the potential to reduce energy consumption cost by approximately 15-30%. Most of the FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data. Based on our experience, faults and relevant events data is very sparse and inadequate, mostly because of the lack of will and incentive for those that would need to keep track of faults. In this paper we introduce the idea of using crowdsourcing to support FDD data collection processes, and illustrate our idea through a mobile application that has been implemented for this purpose. Furthermore, we propose a strategy of how to successfully deploy this building occupants' crowdsourcing application.
引用
收藏
页码:12 / 17
页数:6
相关论文
共 50 条
  • [21] Data Fusion for Fault Diagnosis in Smart Grid Power Systems
    Kordestani, Mojtaba
    Saif, Mehrdad
    2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2017,
  • [22] Fault detection and diagnosis web service module for energy monitoring in buildings
    Melendez, J.
    Burgas, L.
    Gamero, F. I.
    Colomer, J.
    Herraiz, S.
    IFAC PAPERSONLINE, 2018, 51 (10): : 15 - 19
  • [23] Data analytics for smart buildings: a classification method for anomaly detection for measured data
    de la Roy, Enguerrand de Rautlin
    Recht, Thomas
    Zemmari, Akka
    Bourreau, Pierre
    Mora, Laurent
    CARBON-NEUTRAL CITIES - ENERGY EFFICIENCY AND RENEWABLES IN THE DIGITAL ERA (CISBAT 2021), 2021, 2042
  • [24] Control and fault detection in buildings
    Dexter, AL
    ISHVAC 99: 3RD INTERNATIONAL SYMPOSIUM ON HEATING, VENTILATION AND AIR CONDITIONING, VOLS 1 AND 2, 1999, : 39 - 50
  • [25] Combining Performance Testing and Metadata Models to Support Fault Detection and Diagnostics in Smart Buildings
    Markoska, Elena
    Johansen, Aslak
    Kjaergaard, Mikkel Baun
    Lazarova-Molnar, Sanja
    Jradi, Muhyiddine
    Jorgensen, Bo Norregaard
    APPLIED SYSTEM INNOVATION, 2019, 2 (03) : 1 - 19
  • [26] A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings
    Safi, Abdullah
    Ahmad, Zulfiqar
    Jehangiri, Ali Imran
    Latip, Rohaya
    Zaman, Sardar Khaliq Uz
    Khan, Muhammad Amir
    Ghoniem, Rania M.
    SENSORS, 2022, 22 (21)
  • [27] Maintenance of Smart Buildings using Fault Trees
    Cauchi, Nathalie
    Hoque, Khaza Anuarul
    Stoelinga, Marielle
    Abate, Alessandro
    ACM TRANSACTIONS ON SENSOR NETWORKS, 2018, 14 (3-4)
  • [28] One step forward for smart chemical process fault detection and diagnosis
    Bi, Xiaotian
    Qin, Ruoshi
    Wu, Deyang
    Zheng, Shaodong
    Zhao, Jinsong
    COMPUTERS & CHEMICAL ENGINEERING, 2022, 164
  • [29] Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management
    Chew, Michael Yit Lin
    Yan, Ke
    JOURNAL OF SENSORS, 2022, 2022
  • [30] Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges
    Kong, Xiangjie
    Liu, Xiaoteng
    Jedari, Behrouz
    Li, Menglin
    Wan, Liangtian
    Xia, Feng
    IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05): : 8095 - 8113