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A simulation algorithm is proposed that predicts the lighting energy performance of manually and automatically controlled electric lighting and blind systems in private and two-person offices. Algorithm inputs are annual profiles of user occupancy and work plane illuminances. These two inputs are combined with probabilistic switching patterns, which have been derived from field data, in order to predict the status of the electric lighting and blinds throughout the year. The model features four different user types to mimic variation in control behavior between different occupants. An example application in a private office with a southern facade yields that--depending on the user type--the electric lighting energy demand for a manually controlled electric lighting and blind system ranges from 10 to 39 kW h/ [m.sup.2] yr. The predicted mean energy savings of a switch-off occupancy sensor in the example office are 20%. Depending on how reliably occupants switch off a dimmed lighting system, mean electric lighting energy savings due to a daylight-linked photocell control range from 60% to zero. Keywords: Lighting controls; Daylighting; User behavior models

This study investigates the influence of the short-term dynamics of daylight on simulation-based predictions of the annual daylight availability in a building. To this end annual indoor illuminance simulations are carried out for a two-person-office using the RADIANCE-based dynamic daylight simulation method DAYSIM. As of yet, all available daylight simulation methods are typically based on 1-h means of irradiance data and thus tend to neglect the short-term dynamics of daylight. In the first part of this study the dependence of the annual daylight availability on the underlying simulation time step interval is quantified. Assuming two different automated daylight-dependent artificial lighting strategies, the predicted annual artificial lighting demand is systematically underestimated by up to 27% on the simulations based on 1-h means instead of 1-min means of measured beam and diffuse irradiances. The general validity of these results is ensured by employing irradiance data from five stations world-wide. As measured 1-min means of irradiance data are generally not available for practical applications, the stochastic Skartveit-Olseth model, which generates 1-min means of irradiance data from hourly means, is adapted for daylight simulation purposes in the second part of this study. The utilization of modeled 1-min means of irradiance data reduces the above described systematic simulation errors to below 8% for both automated lighting strategies and all five stations. Accordingly, the modified version of the Skartveit-Olseth model is able to enhance the quality of dynamic daylight simulations--without any additional planning effort for the lighting designer.