Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control

Adding advanced behavioural models in whole building energy simulation: A study on the total... Behavioural models derived from on-going field studies can provide the basis for predicting personal action taken to adjust lighting levels, remedy direct glare, and save energy in response to physical conditions. Enabling these behavioural models in advanced lighting simulation programs, such as DAYSIM and the Lightswitch Wizard, allows for a more realistic estimate of lighting use under dynamic conditions. The current downside of these approaches is that the whole building energy impact of manual changes in blind settings and lighting use, including its effect on heating and cooling requirements, is not considered. A sub-hourly occupancy-based control model (SHOCC), which enables advanced behavioural models within whole building energy simulation, is presented. The considered behavioural models are the Lightswitch2002 algorithms for manual and automated light and blind control, while the investigated whole building energy simulation program is ESP-r. The enhanced functionality is demonstrated through annual energy simulations aiming at quantifying the total energy impact of manual control over lights and window blinds. Results show that building occupants that actively seek daylighting rather than systematically relying on artificial lighting can reduce overall primary energy expenditure by more than 40%, when compared to occupants who rely on constant artificial lighting. This underlines the importance of defining suitable reference cases for benchmarking the performance of automated lighting controls. Results also show that, depending on the proportion of buildings occupants that actively seek out daylighting, reduced lighting use through automated control may not always produce anticipated savings in primary energy for indoor climate control. In some cases, reduced lighting use is shown to even increase primary energy expenditure for indoor climate control, trimming down initial primary energy savings in lighting alone. This reveals the superiority of integrated design approaches over simpler daylighting guidelines or rules of thumb. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy and Buildings Elsevier

Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control

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Publisher
Elsevier
Copyright
Copyright © 2006 Elsevier Ltd
ISSN
0378-7788
eISSN
1872-6178
D.O.I.
10.1016/j.enbuild.2006.03.002
Publisher site
See Article on Publisher Site

Abstract

Behavioural models derived from on-going field studies can provide the basis for predicting personal action taken to adjust lighting levels, remedy direct glare, and save energy in response to physical conditions. Enabling these behavioural models in advanced lighting simulation programs, such as DAYSIM and the Lightswitch Wizard, allows for a more realistic estimate of lighting use under dynamic conditions. The current downside of these approaches is that the whole building energy impact of manual changes in blind settings and lighting use, including its effect on heating and cooling requirements, is not considered. A sub-hourly occupancy-based control model (SHOCC), which enables advanced behavioural models within whole building energy simulation, is presented. The considered behavioural models are the Lightswitch2002 algorithms for manual and automated light and blind control, while the investigated whole building energy simulation program is ESP-r. The enhanced functionality is demonstrated through annual energy simulations aiming at quantifying the total energy impact of manual control over lights and window blinds. Results show that building occupants that actively seek daylighting rather than systematically relying on artificial lighting can reduce overall primary energy expenditure by more than 40%, when compared to occupants who rely on constant artificial lighting. This underlines the importance of defining suitable reference cases for benchmarking the performance of automated lighting controls. Results also show that, depending on the proportion of buildings occupants that actively seek out daylighting, reduced lighting use through automated control may not always produce anticipated savings in primary energy for indoor climate control. In some cases, reduced lighting use is shown to even increase primary energy expenditure for indoor climate control, trimming down initial primary energy savings in lighting alone. This reveals the superiority of integrated design approaches over simpler daylighting guidelines or rules of thumb.

Journal

Energy and BuildingsElsevier

Published: Jul 1, 2006

References

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