Quantifying Uncertainties in an Archetype-Based Building Stock Energy Model by Use of Individual Building Models
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Quantifying Uncertainties in an Archetype-Based Building Stock Energy Model by Use of Individual Building Models

Authors: Morten Brøgger, Kim Wittchen

Abstract:

Focus on reducing energy consumption in existing buildings at large scale, e.g. in cities or countries, has been increasing in recent years. In order to reduce energy consumption in existing buildings, political incentive schemes are put in place and large scale investments are made by utility companies. Prioritising these investments requires a comprehensive overview of the energy consumption in the existing building stock, as well as potential energy-savings. However, a building stock comprises thousands of buildings with different characteristics making it difficult to model energy consumption accurately. Moreover, the complexity of the building stock makes it difficult to convey model results to policymakers and other stakeholders. In order to manage the complexity of the building stock, building archetypes are often employed in building stock energy models (BSEMs). Building archetypes are formed by segmenting the building stock according to specific characteristics. Segmenting the building stock according to building type and building age is common, among other things because this information is often easily available. This segmentation makes it easy to convey results to non-experts. However, using a single archetypical building to represent all buildings in a segment of the building stock is associated with loss of detail. Thermal characteristics are aggregated while other characteristics, which could affect the energy efficiency of a building, are disregarded. Thus, using a simplified representation of the building stock could come at the expense of the accuracy of the model. The present study evaluates the accuracy of a conventional archetype-based BSEM that segments the building stock according to building type- and age. The accuracy is evaluated in terms of the archetypes’ ability to accurately emulate the average energy demands of the corresponding buildings they were meant to represent. This is done for the buildings’ energy demands as a whole as well as for relevant sub-demands. Both are evaluated in relation to the type- and the age of the building. This should provide researchers, who use archetypes in BSEMs, with an indication of the expected accuracy of the conventional archetype model, as well as the accuracy lost in specific parts of the calculation, due to use of the archetype method.

Keywords: Building stock energy modelling, energy-savings, archetype.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474571

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References:


[1] T. Loga, B. Stein, and N. Diefenbach, “TABULA building typologies in 20 European countries — Making energy-related features of residential building stocks comparable,” Energy & Buildings, vol. 132, pp. 4–12, 2016. (Online). Available: http://dx.doi.org/10.1016/j.enbuild.2016.06. 094.
[2] P. Caputo, G. Costa, and S. Ferrari, “A supporting method for defining energy strategies in the building sector at urban scale,” Energy Policy, vol. 55, pp. 261–270, 2013. (Online). Available: http://dx.doi.org/10.1016/j.enpol.2012.12.006.
[3] TABULA Project Team, “Typology Approach for Building Stock Energy Assessment - Main Results of the TABULA project,” no. June 2009, p. 43, 2012. (Online). Available: https://ec.europa.eu/energy/intelligent/projects/sites/ iee-projects/files/projects/documents/tabula{ }final{ }report{ }en.pdf.
[4] EPISCOPE, Monitor Progress Towards Climate Targets in European Housing Stocks Main Results of the EPISCOPE Project - Final Project Report - (Deliverable D1.2), 2016, no. March 2016. (Online). Available: http://episcope.eu/monitoring/overview/.
[5] K. B. Wittchen, J. Kragh, and A. Søren, Potential heat savings during ongoing renovations of buildings until 2050. Danish Building Research Institute, Aalborg University, 2016. (Online). Available: http://sbi.dk/Assets/ Potential-heat-savings-during-ongoing-renovations-of-buildings-until-\ 2050/sbi-2016-04-1.pdf.
[6] A. A. Famuyibo, A. Duffy, and P. Strachan, “Developing archetypes for domestic dwellings - An Irish case study,” Energy and Buildings, vol. 50, pp. 150–157, 2012.
[7] Danish Energy Agency (Energistyrelsen), “Bekendtgørelse om H˚andbog for Energikonsulenter (In Danish only),” 2016. (Online). Available: https://www.retsinformation.dk/Forms/R0710.aspx?id=176520.
[8] N. Holck, I. Sartori, M. I. Vestrum, and H. Brattebø, “Explaining the historical energy use in dwelling stocks with a segmented dynamic model : Case study of Norway 1960 – 2015,” Energy & Buildings, vol. 132, pp. 141–153, 2016. (Online). Available: http://dx.doi.org/10.1016/j.enbuild.2016.05.099.
[9] ´ E. Mata, A. Sasic Kalagasidis, and F. Johnsson, “Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK,” Building and Environment, vol. 81, pp. 270–282, 2014. (Online). Available: http://dx.doi.org/10.1016/j.buildenv.2014.06.013.
[10] E. EN/ISO 13790, “Energy performance of buildings–Calculation of energy use for space heating and cooling (EN ISO 13790: 2008),” European Committee for Standardization (CEN), Brussels, 2008.
[11] M. Brøgger and K. B. Wittchen, “Flexible building stock modelling with array-programming Data description Method – array-based programming,” in Proceedings of the 15th IBPSA Conference San Francisco, CA, USA, Aug. 7-9, 2017, Charles S. Barnaby and Michael Wetter, Ed. San Francisco. USA: International Building Performance Simulation Association, 2017, pp. 1027–1036. (Online). Available: http://www.ibpsa.org/?page{ }id=962{#}building-stock.
[12] J. Kragh and K. Wittchen, “Development of two Danish building typologies for residential buildings,” Energy and Buildings, vol. 68, pp. 79–86, 2014. (Online). Available: http://linkinghub.elsevier.com/ retrieve/pii/S037877881300604X.
[13] K. Duer, S. Svendsen, M. M. Mogensen, and J. Birck, “Energy Labelling of Glazings and Windows in Denmark: Calculated and Measured Values,” Solar Energy, vol. 73, no. 1, pp. 23–31, 2002.
[14] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013. (Online). Available: http://www.R-project.org/.
[15] H. Wickham, ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009. (Online). Available: http://ggplot2.org.
[16] ——, tidyverse: Easily Install and Load ’Tidyverse’ Packages, 2017, r package version 1.1.1. (Online). Available: https://CRAN.R-project. org/package=tidyverse.