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Daylighting and energy analysis for air-conditioned office buildings

Authors: Joseph C. Lam; Danny H.W. Li;
Abstract
We propose a simple method for estimating the likely energy savings in electric lighting due to daylighting and the possible cooling penalty. Fractions of the working year and cooling season when daylighting alone is adequate to provide the indoor design illuminance are presented for on-off and top-up controls. Based on the simple average daylight factor method, energy savings in electric lighting have been estimated for a generic office building using measured outdoor illuminance data in Hong Kong. The daylight-induced cooling penalty is estimated using average solar heat gain factors. Our case study suggests that daylighting schemes can result in substantial energy savings in air-conditioned office buildings in Hong Kong.
Related Organizations
- City University of Hong Kong China (People's Republic of)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
20
Average
Top 10%
Average