Extreme heat events have become more intense and severe in the twenty-first century, posing a substantial hazard to human health. Most cities across the globe are affected by urban overheating (UO), which is one of the most well-documented local-scale climate change phenomena. 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. The predicted mean energy savings of a switch-off occupancy sensor in the example office are 20%. ![]() 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/m2 yr. 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. ![]() ![]() Algorithm inputs are annual profiles of user occupancy and work plane illuminances. 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.
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