Data-driven bottom-up approach for modelling internal loads in building energy simulation using functional principal components. Ward, R., Choudhary, R., Heo, Y., & Guillas, S. 09 2016.
abstract   bibtex   
Internal loads in a building are difficult to quantify efficiently in a way which envelopes existing demand yet permits estimation of the impact of changes in building operation. The standard characterisation by energy-use intensity and diversity profile is well established; while quantification of energy-use intensity is achievable using monitored data, there is no standard approach for quantification of diversity profiles. This paper investigates an efficient method for the representation of the shape of the diversity profile using a functional data analysis approach together with electricity consumption data monitored at a spatial resolution that permits correlation of consumption with space use type. The approach has been applied to a case study building and has been shown to give a good agreement with monitored electricity consumption data.
@conference{Ward2016Data-drivenbottom-up,
author = {Rebecca Ward and Ruchi Choudhary and Yeonsook Heo and Serge Guillas},
booktitle = {Building Simulation and Optimisation 2016},
address = {Newcastle},
title = {Data-driven bottom-up approach for modelling internal loads in building energy simulation using functional principal components},
year = {2016},
month = {09},
abstract = {Internal loads in a building are difficult to quantify efficiently in a way which envelopes existing demand yet permits estimation of the impact of changes in building operation. The standard characterisation by energy-use intensity and diversity profile is well established; while quantification of energy-use intensity is achievable using monitored data, there is no standard approach for quantification of diversity profiles. This paper investigates an efficient method for the representation of the shape of the diversity profile using a functional data analysis approach together with electricity consumption data monitored at a spatial resolution that permits correlation of consumption with space use type. The approach has been applied to a case study building and has been shown to give a good agreement with monitored electricity consumption data.
},
project = {b-bem}
}

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