Lightswitch-2002: a model for manual and automated control of electric lighting and blinds
C. Reinhart. A version of this document is published in Solar Energy, v. 77, no. 1, 2004, pp. 15-28Review by Scott Schuetter, Energy Center of Wisconsin:
The IESNA Lighting Handbook indicates that lighting control performance information is not readily available and that it depends on indoor daylight levels, size of space, work schedules, and occupant attitude and training. In this paper, Reinhardt develops a simulation algorithm for predicting electric lighting demand and blind usage for manually and automatically controlled systems in private and two-person offices. This algorithm is dynamic in that it uses 5 minute time steps to assess the occupancy and illuminance levels of the space. It is also stochastic, making on/off decisions regarding the lighting controls every time a user is faced with a control decision.
Three levels of blind control are analyzed. Automated blind control comprises the blinds automatically being lowered when incoming solar irradiance is above 50 W/m2 and fully opened otherwise. Dynamic manual blind control comprises the blinds automatically being lowered when incoming solar irradiance is above 50 W/m2 and fully opened every morning. Static manual blind control involves the blinds being permanently lowered.
The algorithm was used to model the electric lighting usage in a private office on a south-facing facade in Toronto, Canada. The office schedule was weekdays from 8:00 am to 6:00 pm and the lighting power density of the space was 15 W/m2 (1.4 W/ft2). The study showed that an occupancy sensor showed energy savings of 20%, but prevents users from utilizing available daylight. Controlling the electric lights with photosensors can save up to 60% of electric energy usage. However, this savings can be completely negated if it is not coupled with an occupancy sensor or time-clock control since the electric lights are often left on overnight.