Multivariate Methods for Evaluating Building Energy Efficiency
2004 (English)In: Proceedings of the 2004 ACEEE Summer Study of Energy Efficiency in Buildings, 2004, 265-274 p.Conference paper (Refereed)
For an owner or operator of a building, benchmarking can be a useful guide for finding outhow energy efficient the building is and identifying what to improve. For successfulevaluation of the building energy efficiency, the categorization as well as the parameteridentification has decisive importance. That selection can be based on mathematical modelingsuch as linear regression accompanied with more or less user expert knowledge. Theselection, however, is not a simple task since analyses based on statistical data are sensitive tocorrelations between different measured parameters. For improving that analysis multivariatemethods such as Principal Component Analysis (PCA) can be a valuable support.We demonstrate here how PCA can be a useful tool for investigating aggregated statisticaldatasets. The investigation illustrates how a set of building performance parameters exhibitsdifferent relations depending on how the categorization is made, which is relevant to considerwhen benchmarking. The study is based on a national Swedish database of aggregated energyuse and building performance statistics.
Place, publisher, year, edition, pages
2004. 265-274 p.
IdentifiersURN: urn:nbn:se:umu:diva-20202ISBN: 0-918249-53-8OAI: oai:DiVA.org:umu-20202DiVA: diva2:208241