On adaptive occupant-learning window blind and lighting controls

Abstract

Occupants have a significant impact upon building energy use, e.g. through the actuation of window blinds and switching off lights. Automation systems with fixed set points for controlling blinds and lights have been used in some applications as an attempt to mitigate the impact of occupant behaviour upon energy consumption. A conceptual framework of an alternative control method is presented, one in which the control system adapts control set points in real time to each occupant's preferences. The potential of this hypothesis is demonstrated through a simulation-based study focused on a hypothetical south-facing office with existing empirical models that predict occupant behaviour regarding the control of window blinds and lights. The performance of a proposed adaptive automation system is simulated, one in which window-blind and lighting control set points are adapted in real time to learn the modelled occupant preferences using a Kalman filter. The performance of this alternative occupant-learning method of control is contrasted to that of two conventional control methods, one in which occupants have manual control over window blinds and lights, and the other that employs an automation system with fixed set points. The simulation results indicate that such an adaptive occupant-learning control method could lead to substantial energy savings.