A differentiated description of building-stocks for a georeferenced urban bottom-up building-stock model

Several building-stock modelling techniques have been employed to investigate the impact of energy efficiency measures (EEM), where the description of the building-stock generally consists of an age-type classification to specify building characteristics for groups of buildings. Such descriptions lack the appropriate level of detail to differentiate the potential for EEM within age groups. This paper proposes a methodology for building-stock description using building-specific data and measured energy use to augment an age-type building-stock classification. By integrating building characteristics from energy performance certificates, measured energy use and envelope areas from a 2.5D GIS model, the building-stock description reflects the heterogeneity of the building-stock. The proposed method is validated using a local building portfolio (N = 433) in the city of Gothenburg, where modelled results for space heating and domestic hot water are compared to data from measurements, both on an individual building level and for the entire portfolio. Calculated energy use based on the building-stock description of the portfolio differ less than 3% from measured values, with 42% of the individual buildings being within a 20% margin of measured energy use indicating further work is needed to reduce or quantify the uncertainty on a building level.

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

[2]  Holger Wallbaum,et al.  Handling data uncertainties when using Swedish energy performance certificate data to describe energy usage in the building stock , 2015 .

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

[4]  Niko Heeren,et al.  A component based bottom-up building stock model for comprehensive environmental impact assessment and target control , 2013 .

[5]  Steven K. Firth,et al.  Dynamic energy modelling of UK housing: evaluation of alternative approaches , 2013 .

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

[7]  Henk Visscher,et al.  Actual and theoretical gas consumption in Dutch dwellings: What causes the differences? , 2013 .

[8]  Jesper Kragh,et al.  Development of two Danish building typologies for residential buildings , 2014 .

[9]  Cecilia Björk,et al.  Så byggdes husen 1880-1980 : arkitektur, konstruktion och material i våra flerbostadshus under 100 år , 1984 .

[10]  Agis M. Papadopoulos,et al.  Assessment of retrofitting measures and solar systems' potential in urban areas using Geographical Information Systems: Application to a Mediterranean city , 2012 .

[11]  Filip Johnsson,et al.  A modelling strategy for energy, carbon, and cost assessments of building stocks , 2013 .

[12]  F. Johnsson,et al.  Cost-effective retrofitting of Swedish residential buildings: effects of energy price developments and discount rates , 2015 .

[13]  Sofia Stensson.  Energy Efficiency in Shopping Malls - Some Aspects Based on a Case Study , 2014 .

[14]  Philip James,et al.  A GIS domestic building framework to estimate energy end-use demand in UK sub-city areas , 2015 .

[15]  Kristian Fabbri,et al.  Heritage buildings and energy performance: Mapping with GIS tools , 2012 .

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

[17]  Shem Heiple,et al.  Using building energy simulation and geospatial modeling techniques to determine high resolution building sector energy consumption profiles , 2008 .

[18]  P Steadman.  Research on building stocks , 2009 .

[19]  Angela Sasic Kalagasidis,et al.  A statistical method for assessing retrofitting measures of buildings and ranking their robustness against climate change , 2015 .

[20]  O. Edenhofer,et al.  Climate change 2014 : mitigation of climate change , 2014 .

[21]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[22]  Filip Johnsson,et al.  Modelling opportunities and costs associated with energy conservation in the Spanish building stock , 2015 .

[23]  F. Stazi,et al.  Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam , 2014 .

[24]  Angela Sasic Kalagasidis,et al.  Calculation of the energy use in the Swedish housing. Description of the building energy simulation model: EABS Energy Assessment of Building Stocks. , 2009 .

[25]  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 .

[26]  Gian Vincenzo Fracastoro,et al.  A methodology for assessing the energy performance of large scale building stocks and possible appli , 2011 .

[27]  David J. Spiegelhalter,et al.  Handling uncertainty in housing stock models , 2012 .

[28]  Maria Kolokotroni,et al.  A GIS-based bottom-up space heating demand model of the London domestic stock , 2009 .

[29]  Niklaus Kohler,et al.  Building age as an indicator for energy consumption , 2015 .

[30]  Claudio Nägeli,et al.  SPATIAL BUILDING STOCK MODELLING TO ASSESS ENERGY- EFFICIENCY AND RENEWABLE ENERGY IN AN URBAN CONTEXT , 2013 .

[31]  Qian Wang,et al.  The Application of LCCA toward Industrialized Building Retrofitting − Case Studies of Swedish Residential Building Stock , 2013 .

[32]  Maurizio Sasso,et al.  Analysis and diagnosis of the energy performance of buildings and districts: Methodology, validation and development of Urban Energy Maps , 2013 .

[33]  Filip Johnsson,et al.  Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK , 2014 .

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

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