Development of smart lighting systems can be considered under different perspectives. While a main business case for smart lighting is energy savings, another important consideration is the level of visual comfort experienced by the occupants. The latter aspect has been documented mainly in the context of the circadian system. A growing body of knowledge is related to the environmental impact of energy consumption related to lighting. The so-called layered lighting design, which aims at an effective combination of artificial and natural light, plays a key role in formal certification procedures for building design. This thesis presents two main contributions to the challenging area of the lighting industry: First, a lighting control scheme is proposed which integrates artificial lighting and daylight harvesting. Secondly, the development of an application programming interface is presented which allows one to integrate a control scheme with a simulated scene. The latter part of the contribution could be particularly beneficial for quasi-real-time validation of a lighting control algorithm against a virtual environment. The simulation environment can be deployed on a cloud environment. The case study discussed in this thesis is a scaled down version of an open-plan office lit through a set of individually addressable LED luminaries. It is further assumed that a second source of luminous flux is available in the form of natural light. The control problem consists of maintaining a level of illuminance which meets the users' requirements while minimizing the energy consumption. The proposed intelligent lighting system is based on an adaptive multivariable control scheme where the parameters of the model are determined through a simple identification procedure. The behaviour of the control system was validated through simulation studies and tested on a small scale room. Test results clearly show that a smart lighting system designed around the layered lighting design paradigm is indeed a compelling business case.
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Thesis advisor: Moallem, Mehrdad
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