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Weber, Tim Tim. Empirical parameter studies on the heating energy use in swedish and german low-energy houses. International Journal Of Low Energy And Sustainable Buildings, Stockholm, v. 3, 28, 2005.
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Abstract

Using statistical methods as well as physical relations derived from classical engineering studies, measurements of 44 Swedish low-energy constructions with a total of more than 300 houses and 37 German constructions with a total of more than 50 houses were statistically analysed. The resulting parameter study is an effort to empirically detect those parameters that have a significant influence on the purchased heating energy use in buildings. Applying well- known physical relations to the statistical model leads to a simplification of the statistical model and each simplification is statistically tested to verify empirically the used physical relation in. The results show that almost all of the purchased heating energy use of a building can be explained with only seven parameters. The remaining standard deviation is moderate compared to a theoretical statistical study. This standard deviation can have several explanations, both building and user related. It is still not possible to exactly quantify the user’s influence on the purchased heating energy, but it seems to be much smaller than usually claimed if the relevant parameters such as e.g. ventilation rate can be controlled in a sufficient way. The remaining standard deviation does not play a role if calculating the energy reduction in low-energy houses on an average level but it becomes a crucial point if it is discussed on an individual level. Especially for buildings designed to be a zero-heating house the standard deviation could play a role leading to a poor thermal quality of the designed house. For energy calculations it would be more precise to give the mean value and its standard deviation due to uncertainties in design and construction. If it is possible to ensure a design and a construction work with low uncertainties it should be possible to reduce the standard deviation to it’s user and climate related minimum.
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