A data driven approach for smart lighting

  1. Get@NRC: A data driven approach for smart lighting (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1007/978-3-319-07467-2_33
AuthorSearch for: ; Search for: ; Search for:
TypeBook Chapter
Proceedings titleModern Advances in Applied Intelligence : 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part II
Series titleLecture Notes In Computer Science; Volume 8482
Conference27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2014), June 3-6, 2014, Kaohsiung, Taiwan
Pages308317; # of pages: 10
SubjectEnergy utilization; Intelligent systems; Office buildings; Classification informations; Collective classifications; Commercial building; Data-driven approach; Data-driven methods; Light requirements; Lighting applications; Lighting policies
AbstractSmart lighting for commercial buildings should consider both the overall energy usage and the occupants' individual lighting preferences. This paper describes a study of using data mining techniques to attain this goal. The lighting application embraces the concept of Office Hotelling, where employees are not assigned permanent office spaces, but instead a temporary workplace is selected for each check-in staff. Specifically, taking check-in workers' light requirements as inputs, a collective classification strategy was deployed, aiming at simultaneously predicting the dimming levels of the shared luminaries in an open office sharing light. This classification information, together with the energy usages for possible office plans, provides us with lighting scenarios that can both meet users' lighting comfort and save energy consumption. We compare our approach with four other commonly used lighting control strategies. Our experimental study shows that the developed learning model can generate lighting policies that not only maximize the occupants' lighting satisfaction, but also substantially improve energy savings. Importantly, our data driven method is able to create an optimal lighting scenario with execution time that is suitable for a real-time responding system.
Publication date
PublisherSpringer International Publishing
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21272695
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Record identifiere949bf25-fbfd-4e80-a875-551b2442a5c6
Record created2014-12-03
Record modified2016-06-21
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