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Library Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use

Resource information

Date of publication
December 2012
Resource Language
ISBN / Resource ID
AGRIS:US201600059862
Pages
65-72

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor’s parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO₂ emissions if additional factors are considered.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Zhou, Yuyu
Weng, Qihao
Gurney, Kevin R.
Shuai, Yanmin
Hu, Xuefei

Publisher(s)
Data Provider
Geographical focus