A Review of the Status of Uncertainty and Sensitivity Analysis in Building-stock Energy Models

Building-Stock Energy Models (BSEMs) are emerging as a powerful tool for cities and regions seeking to reduce greenhouse gas emissions and mitigate the effects of changing climates for their populations. The potential influence of such model results coupled with the scale and complexity of the environments they aim to represent means it is essential to understand their limitations. This study undertakes a systematic review of the literature relating to such models and finds that in only a very small proportion of studies are model uncertainties even considered. This fundamental flaw is due to the computational demands of exploring the output space of such complex models. A more detailed assessment was then undertaken of the identified studies in which uncertainty analysis (UA) and sensitivity analysis (SA) had been applied to BSEMs. The adequacy of the applied methods is discussed, and recommendations proposed for the application of best practice techniques based on the underlying form of the model.

[1]  Dirk Saelens,et al.  Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation , 2012 .

[2]  M. S. De Wit,et al.  Identification of the important parameters in thermal building simulation models , 1997 .

[3]  Volker Coors,et al.  Citygml-based 3d City Model For Energy Diagnostics And Urban Energy Policy Support , 2013, Building Simulation Conference Proceedings.

[4]  Wei Tian,et al.  A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London , 2012 .

[5]  Andrea Saltelli,et al.  From screening to quantitative sensitivity analysis. A unified approach , 2011, Comput. Phys. Commun..

[6]  G. Augenbroe,et al.  Analysis Methodology for Large Organizations' Investments in Energy Retrofit of Buildings , 2011 .

[7]  Qingyan Chen,et al.  Impact of climate change heating and cooling energy use in buildings in the United States , 2014 .

[8]  Carlos Cerezo Davila,et al.  Building archetype calibration for effective urban building energy modeling , 2017 .

[9]  B. Croke,et al.  Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R , 2013 .

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

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

[12]  Jianhua Zhao,et al.  Development of a Novel Statistical Method and Procedure for Material Characterization and a Probabilistic Approach to Assessing the Hygrothermal Performance of Building Enclosure Assemblies , 2012 .

[13]  François Maréchal,et al.  City Energy Analyst (CEA): Integrated framework for analysis and optimization of building energy systems in neighborhoods and city districts , 2016 .

[14]  David J. Spiegelhalter,et al.  A hierarchical Bayesian framework for calibrating micro-level models with macro-level data , 2013 .

[15]  Jens Christian Refsgaard,et al.  Modelling guidelinesterminology and guiding principles , 2004 .

[16]  Marjorie Musy,et al.  Application of sensitivity analysis in building energy simulations: combining first and second order elementary effects Methods , 2012, ArXiv.

[17]  Sukumar Natarajan,et al.  Climate change and future energy consumption in UK housing stock , 2010 .

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

[19]  Gerardo Maria Mauro,et al.  Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach , 2017 .

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

[21]  Paola Annoni,et al.  Sixth International Conference on Sensitivity Analysis of Model Output How to avoid a perfunctory sensitivity analysis , 2010 .

[22]  Zheng O'Neill,et al.  Uncertainty and sensitivity decomposition of building energy models , 2012 .

[23]  Zhiqiang John Zhai,et al.  Review on stochastic modeling methods for building stock energy prediction , 2017 .

[24]  M. Perino,et al.  Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study , 2017 .

[25]  Frank Schultmann,et al.  From the Building Level Energy Performance Assessment to the National Level: How are Uncertainties Handled in Building Stock Models , 2017 .

[26]  Tuğçe Kazanasmaz,et al.  Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation , 2014 .

[27]  Helge Brattebø,et al.  Sensitivity analysis in long-term dynamic building stock modeling—Exploring the importance of uncertainty of input parameters in Norwegian segmented dwelling stock model , 2014 .

[28]  Rokia Raslan,et al.  Results variability in accredited building energy performance compliance demonstration software in the UK: an inter-model comparative study , 2010 .

[29]  G. Deman,et al.  Sensitivity analysis approaches to high-dimensional screening problems at low sample size , 2018 .

[30]  Koen Steemers,et al.  Modelling domestic energy consumption at district scale: A tool to support national and local energy policies , 2011, Environ. Model. Softw..

[31]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[32]  G. Papaefthymiou,et al.  Potential of Heat Pumps for Demand Side Management and Wind Power Integration in the German Electricity Market , 2012, IEEE Transactions on Sustainable Energy.

[33]  Philip Steadman,et al.  3DStock: A new kind of three-dimensional model of the building stock of England and Wales, for use in energy analysis , 2017 .

[34]  A. Rasheed,et al.  CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning , 2009 .

[35]  José Manuel Cejudo López,et al.  Uncertainties and sensitivity analysis in building energy simulation using macroparameters , 2013 .

[36]  P Pieter-Jan Hoes,et al.  Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy , 2016 .

[37]  Warren E. Walker,et al.  Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support , 2003 .

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

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

[40]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[41]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[42]  Xiaobo Zhou,et al.  Global Sensitivity Analysis , 2017, Encyclopedia of GIS.

[43]  I. Cartuja.  ON SCIENCE AND PRECAUTION IN THE MANAGEMENT OF TECHNOLOGICAL RISK , 2000 .

[44]  William Becker,et al.  Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices , 2017, Environ. Model. Softw..

[45]  Volker Coors,et al.  The influence of data quality on urban heating demand modeling using 3D city models , 2017, Comput. Environ. Urban Syst..

[46]  Y. Shimoda,et al.  Residential end-use energy simulation at city scale , 2004 .