Building energy parameter investigations based on multivariate analysis
2009 (English)In: Energy and Buildings, ISSN 0378-7788, Vol. 41, 71-80 p.Article in journal (Refereed) Published
System identification can be used for evaluations of how measured energy use is influenced by theoperation, design and equipment of buildings and their users. However, it can be difficult to accessappropriate data for modelling purposes due to a small number of buildings, parameters not distributednormally, lumped information, etc. In this work, data of a subset of 112 comparable multifamily buildingslocated in the Stockholm area were derived from a larger Swedish building energy consumption survey.In that database, the accessible data are monthly consumption data together with a large number ofbuilding-specific classification parameters, e.g. building code, age of control system, type of owner,maintenance organization, area to let, etc.A multivariate PLS method (partial least squares to latent structures) was used to model differentenergy performance measures, such as the use of energy for heating, electricity used to operate thebuilding technical system, the building total heat loss coefficient and the use of domestic cold water. ThePLS model was investigated for both the total annual use and the annual use normalized to the availablefloor area. For most measures of performance, only qualitative estimates of the impact of differentclassification parameters could be drawn due to the goodness value of the model. However, for some ofthe investigated parameters, quantitative estimates could also be drawn. The obtained results are, inmost cases, in good agreement with what might be expected.To enable benchmarking of different energy use measures, the area to let is commonly used as anormalizer by real estatemanagers in Sweden. In this study, we find strong indications that the area to letis not suitable for this purpose.
Place, publisher, year, edition, pages
Elsevier , 2009. Vol. 41, 71-80 p.
Energy parameters, System identification, Residential buildings, Multivariate analysis, PLS
IdentifiersURN: urn:nbn:se:umu:diva-20064DOI: doi:10.1016/j.enbuild.2008.07.012OAI: oai:DiVA.org:umu-20064DiVA: diva2:208042