Building age as an indicator for energy consumption

Current energy consumption estimates for building stocks rarely use measured data. Calculated data of supposed representative buildings are extrapolated to a wider building stock. Conclusions drawn about supposed similar energy consumption levels risk being misleading when considering older buildings. The chosen approach is based on detailed morphological information of the building stock of a Swiss city, and of the final energy consumption (gas) of all buildings in two districts. The basic data provided from a number of different sources were aggregated in a GIS-based depository. An unbiased analysis of the building stock reduces the risk of misinterpreting consumption patterns and of adopting inappropriate renovation strategies, which could lead to the irreversible loss of tangible and intangible values. The findings of this contribution question the general explanatory power of typologies as a priori classifications by construction age and dwelling type. Using an evidence-based methodology, the significance of all available building attributes were analysed to decide if classification is at all a feasible method, and, if so, which aspects should be considered. A statistical method (CHAID) showed that there is a strong interdependence between energy consumption, compactness, and building age. Buildings constructed before 1921 performed better than the stock average. Buildings built between 1947 and 1979, which constitute the largest share of stock, performed worse. Better knowledge and understanding of the actual energy performance of the stock could foster differentiated planning policies; primarily focusing on those segments of the building stock with high energy saving potentials. This would allow adapting renovation targets for other more vulnerable segments of the stock. The approach presented here could facilitate a controlled improvement of the existing stock, and allow identifying both successful and less successful renovation strategies.

[1]  Yuehong Lu,et al.  Method and case study of quantitative uncertainty analysis in building energy consumption inventories , 2013 .

[2]  C. Ratti,et al.  Energy consumption and urban texture , 2005 .

[3]  H. Brattebø,et al.  Analysis of energy and carbon flows in the future Norwegian dwelling stock , 2012 .

[4]  Vijay Modi,et al.  Spatial distribution of urban building energy consumption by end use , 2012 .

[5]  Anne Grete Hestnes,et al.  Energy demand in the Norwegian building stock: Scenarios on potential reduction , 2009 .

[6]  Piljae Im,et al.  Comparison of building energy use data between the United States and China , 2014, Energy and Buildings.

[7]  Nianping Li,et al.  Statistical analyses on winter energy consumption characteristics of residential buildings in some c , 2010 .

[8]  Henggen Shen,et al.  Determining sample size for building energy consumption surveys using statistical theory , 2012 .

[9]  M. Assimakopoulos,et al.  On the relation between the energy and social characteristics of the residential sector , 2007 .

[10]  Uta Hassler,et al.  Alternative scenarios for energy conservation in the building stock , 2012 .

[11]  M. Feldesman,et al.  Classification trees as an alternative to linear discriminant analysis. , 2002, American journal of physical anthropology.

[12]  Robert Lowe,et al.  Policy and strategy challenges for climate change and building stocks , 2009 .

[13]  Tuğçe Kazanasmaz,et al.  On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir , 2014 .

[14]  A. Wright,et al.  Targeting household energy-efficiency measures using sensitivity analysis , 2010 .

[15]  Ray Galvin,et al.  Introducing the prebound effect: the gap between performance and actual energy consumption , 2012 .

[16]  Stelios Zerefos,et al.  The role of building form in energy consumption: The case of a prismatic building in Athens , 2012 .

[17]  Ray Galvin,et al.  Making the ‘rebound effect’ more useful for performance evaluation of thermal retrofits of existing homes: Defining the ‘energy savings deficit’ and the ‘energy performance gap’ , 2014 .

[18]  Celina Filippín,et al.  Evaluation of heating energy consumption patterns in the residential building sector using stepwise selection and multivariate analysis , 2013 .

[19]  S. Salat.  Energy loads, CO2 emissions and building stocks: morphologies, typologies, energy systems and behaviour , 2009 .

[20]  Lorenzo Belussi,et al.  Method for the prediction of malfunctions of buildings through real energy consumption analysis: Holistic and multidisciplinary approach of Energy Signature , 2012 .

[21]  Håvard Bergsdal,et al.  Historical energy analysis of the Norwegian dwelling stock , 2011 .

[22]  Claus Michelsen,et al.  Energieeffizienz im Altbau: Werden die Sanierungspotenziale überschätzt? Ergebnisse auf Grundlage des ista-IWH-Energieeffizienzindex , 2010 .

[23]  Wolf Fichtner,et al.  Energy efficiency in the German residential sector: A bottom-up building-stock-model-based analysis in the context of energy-political targets , 2013 .

[24]  M. N. Assimakopoulos,et al.  Using intelligent clustering techniques to classify the energy performance of school buildings , 2007 .

[25]  Henk Visscher,et al.  Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: Discrepancies and policy implications , 2013 .

[26]  Andreas Uihlein,et al.  Policy options towards an energy efficient residential building stock in the EU-27 , 2010 .

[27]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

[28]  Giuliano Dall'O',et al.  A methodology for the energy performance classification of residential building stock on an urban scale , 2012 .

[29]  Ian Hamilton,et al.  Energy and urban built form: an empirical and statistical approach , 2014 .

[30]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[31]  S. Evans,et al.  Wall area, volume and plan depth in the building stock , 2009 .

[32]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[33]  R. M. Cormack,et al.  A Review of Classification , 1971 .

[34]  Veselina Grigorova.  Innovations in Energy – Factor for Increasing the Energy Efficiency , 2012 .

[35]  Serge Salat,et al.  Building energy models and assessment systems at the district and city scales: a review , 2012 .