TY - JOUR
T1 - Mobile Crowdsourcing of Occupant Feedback in Smart Buildings
AU - Lazarova-Molnar, Sanja
AU - Pór Logason, Halldór
AU - Andersen, Peter Grønbæk
AU - Kjærgaard, Mikkel Baun
PY - 2017
Y1 - 2017
N2 - Energy consumption of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas include rigorous measures aimed at reducing the energy consumption and, thereby, the 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 FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data about faults and related events. Based on our experience, such data is very sparse and inadequate, mostly because of the difficulty and lack of incentive to collect such data in a structured manner. In this article we introduce the idea of using crowdsourcing to support FDD-related data collection, and illustrate the concept through a mobile application that has been implemented for this purpose. Furthermore, we describe our experience from using the mobile application in a university building and propose a strategy of how to successfully deploy the application in new buildings.
AB - Energy consumption of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas include rigorous measures aimed at reducing the energy consumption and, thereby, the 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 FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data about faults and related events. Based on our experience, such data is very sparse and inadequate, mostly because of the difficulty and lack of incentive to collect such data in a structured manner. In this article we introduce the idea of using crowdsourcing to support FDD-related data collection, and illustrate the concept through a mobile application that has been implemented for this purpose. Furthermore, we describe our experience from using the mobile application in a university building and propose a strategy of how to successfully deploy the application in new buildings.
U2 - 10.1145/3090058.3090060
DO - 10.1145/3090058.3090060
M3 - Journal article
SN - 1559-6915
VL - 17
SP - 5
EP - 14
JO - Applied Computing Review
JF - Applied Computing Review
IS - 1
ER -