TY - GEN
T1 - Mobile crowdsourcing of data for fault detection and diagnosis in smart buildings
AU - Lazarova-Molnar, Sanja
AU - Logason, Halldór Pór
AU - Andersen, Peter Grønbæk
AU - Kjærgaard, Mikkel Baun
PY - 2016/10/11
Y1 - 2016/10/11
N2 - 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. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
AB - 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. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
KW - Buildings
KW - Crowdsourcing
KW - Data collection
KW - Energy performance
KW - Fault detection and diagnosis
KW - Occupants
UR - https://www.scopus.com/pages/publications/85006817862
UR - https://www.scopus.com/pages/publications/85006817862#tab=citedBy
U2 - 10.1145/2987386.2987416
DO - 10.1145/2987386.2987416
M3 - Conference contribution
AN - SCOPUS:85006817862
T3 - Proceedings of the 2016 Research in Adaptive and Convergent Systems, RACS 2016
SP - 12
EP - 17
BT - Proceedings of the 2016 Research in Adaptive and Convergent Systems, RACS 2016
PB - Association for Computing Machinery, Inc
T2 - 2016 Research in Adaptive and Convergent Systems, RACS 2016
Y2 - 11 October 2016 through 14 October 2016
ER -
