TY - GEN
T1 - Intelligent illumination model-based lighting control
AU - Fischer, Michael
AU - Wu, Kui
AU - Agathoklis, Pan
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2012
Y1 - 2012
N2 - Lighting in commercial office environments is a major factor in workplace comfort, productivity, and stress. Modern work environments strive to improve these conditions by better selection of luminaires (fixtures), wall, floor, and furniture colour and texture, and intelligent lighting control that ensures that occupants each receive the proper amount of light needed at their workstations for the task at hand. Daylight infiltration is generally good for occupant comfort, and provides opportunities to "harvest" this daylight and save energy on electric lighting, provided that it is harnessed in a way that it does not over illuminate or cause glare. Building an intelligent lighting control system is challenging due to incompatible or sometimes conflicting lighting preferences from adjacent people or areas. Some office environments can be additionally challenging because of complex geometries, time-varying nondeterministic daylight contributions through windows, and glare. Here we address the challenges of meeting users' individual lighting preferences using a highly accurate illumination model to enable balancing the various lighting requirements among spatially grouped task areas, while minimizing the energy needed to do so (and in the future, minimizing the number of wireless sensors needed for this application). We propose an illumination model-based method and algorithm for intelligent open-loop lighting control, and present the results of a simulation study using a simplistic virtual room model to demonstrate the validity of our method. Daylight infiltration is to be addressed in future work. © 2012 IEEE.
AB - Lighting in commercial office environments is a major factor in workplace comfort, productivity, and stress. Modern work environments strive to improve these conditions by better selection of luminaires (fixtures), wall, floor, and furniture colour and texture, and intelligent lighting control that ensures that occupants each receive the proper amount of light needed at their workstations for the task at hand. Daylight infiltration is generally good for occupant comfort, and provides opportunities to "harvest" this daylight and save energy on electric lighting, provided that it is harnessed in a way that it does not over illuminate or cause glare. Building an intelligent lighting control system is challenging due to incompatible or sometimes conflicting lighting preferences from adjacent people or areas. Some office environments can be additionally challenging because of complex geometries, time-varying nondeterministic daylight contributions through windows, and glare. Here we address the challenges of meeting users' individual lighting preferences using a highly accurate illumination model to enable balancing the various lighting requirements among spatially grouped task areas, while minimizing the energy needed to do so (and in the future, minimizing the number of wireless sensors needed for this application). We propose an illumination model-based method and algorithm for intelligent open-loop lighting control, and present the results of a simulation study using a simplistic virtual room model to demonstrate the validity of our method. Daylight infiltration is to be addressed in future work. © 2012 IEEE.
KW - illumination modeling
KW - intelligent lighting
KW - machine learning
KW - wireless control
UR - http://www.scopus.com/inward/record.url?scp=84866353262&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84866353262&origin=recordpage
U2 - 10.1109/ICDCSW.2012.75
DO - 10.1109/ICDCSW.2012.75
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - 32nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2012
SP - 245
EP - 249
BT - Proceedings - 32nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2012
T2 - 32nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2012
Y2 - 18 June 2012 through 21 June 2012
ER -