Minimizing computational cost and energy demand of building lighting systems: A real time experiment using a modified competition over resources algorithm

Abstract

Lighting systems in buildings have substantial influence on both the occupants' productivity and energy efficiency. This paper addresses the problem of minimizing the energy necessary to provide predefined scheduled lighting levels upon a set of targets through an optimization technique. An experimental setup has been designed, built and used to gather data regarding the influences of five light sources upon three targets. The optimization problems corresponding to different lighting demands were solved with the recently introduced Competition Over Resources bio-inspired metaheuristic optimization algorithm. Modifications on this algorithm were proposed in order to enhance its performance in terms of convergence speed and computational effort. The modified algorithm was firstly tested with classical benchmark functions, and then with a real time lighting problem. Once algorithm performance improvements were verified, the energy consumption over a scheduled lighting demand was measured under different test conditions as to evaluate potential savings whilst minimizing the computational cost of running the algorithm. Using the proposed modifications, the best parameters for each lighting demand condition in the predefined schedule have been identified. By using these parameters along with a strategy of reusing the best solution while the lighting demand does not change, smaller population sizes in the algorithm could be used. Finally, the minimum algorithm population size at which energetic performance begins to be affected was identified. Promising results in terms of energy efficiency and reduced computational effort were achieved.