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Abstract: Electric lighting consumes 19% of total electricity production worldwide. Open-plan offices constitute the majority of office stock, in which most office workers perform their daily tasks, and hence the lighting in those offices has significant impact on, not only energy usage, but also occupants’ productivity. This paper presents a unique lighting optimization approach for open-plan offices that is capable of tuning task lighting to each occupant''s preference in the most energy-efficient fashion. Exploiting individual controllability of the advanced networked lighting systems, each luminaire can be dynamically actuated at a different level to realize various lighting configurations without any physical rewiring. This research formulates lighting actuation into a linear programming problem and finds the optimal light outputs for each of the luminaires that collaboratively deliver specified lighting to each workstation while using the least amount of energy. Simulations using our lighting optimization approach have shown its versatility in providing proper workstation-specific task lighting under different requirements compared to business-as-usual practices. Energy savings are generated from tuning the light levels to occupants’ needs as well as keeping unoccupied areas unlit or minimally lit with this approach. [Copyright &y& Elsevier]

Abstract: This paper describes a large-scale wireless and wired environmental sensor network test-bed and its application to occupancy detection in an open-plan office building. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security; however, the ability to discern the actual number of people in a room is beyond the scope of current sensing techniques. To address this problem, a complex sensor network is deployed in the Robert L. Preger Intelligent Workplace comprising a wireless ambient-sensing system, a wired carbon dioxide sensing system, and a wired indoor air quality sensing system. A wired camera network is implemented as well for establishing true occupancy levels to be used as ground truth information for deriving algorithmic relationships with the environment conditions. To our knowledge, this extensive and diverse ambient-sensing infrastructure of the ITEST setup as well as the continuous data-collection capability is unprecedented. Final results indicate that there are significant correlations between measured environmental conditions and occupancy status. An average of 73% accuracy on the occupancy number detection was achieved by Hidden Markov Models during testing periods. This paper serves as an exploration to the research of ITEST for occupancy detection in offices. In addition, its utility extends to a wide variety of other building technology research areas such as human-centered environmental control, security, energy efficient and sustainable green buildings. [Copyright &y& Elsevier]