A multi-sectoral approach to modelling community energy demand of the built environment

Abstract This paper examines the major challenges associated with evaluating energy demand in the residential building sector in an integrated energy system modelling environment. Three established modelling fields are examined to generate a framework for assessing the impact of energy policy: energy system models, building stock models and dynamic building simulation. A set of profound challenges emerge when attempting to integrate such models, due to distinct differences in their intended applications, operational scales, formulations and computational implementations. Detailed discussions are provided on the integration of temporally refined energy demand, based on thermodynamic processes and socio-technical effects which may stem from new policy. A detailed framework is discussed for generating aggregate residential demands, in terms of space heating demand, domestic hot water demand, and lighting, appliance and consumer electronics demand. The framework incorporates a pathway for interpreting the effects of changes in household behaviour resulting from prospective policy measures. When long-term planning exercises are carried out using this framework, the cyclic effects between behavioural change and policy implementation are also considered. This work focused specifically on the United Kingdom energy system, however parallels can be drawn with other countries, in particular those with a mature privatised system, dominated by space heating concerns.

[1]  I. Hamilton,et al.  Empirical variation in 24-h profiles of delivered power for a sample of UK dwellings: Implications for evaluating energy savings , 2015 .

[2]  David Fischer,et al.  A stochastic bottom-up model for space heating and domestic hot water load profiles for German households , 2016 .

[3]  E. Voet,et al.  How to deal with the rebound effect? A policy-oriented approach , 2016 .

[4]  Alastair R. Buckley,et al.  A review of energy systems models in the UK: Prevalent usage and categorisation , 2016 .

[5]  Maryse Labriet,et al.  ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure , 2008, Comput. Manag. Sci..

[6]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[7]  Tadj Oreszczyn,et al.  Energy efficiency uptake and energy savings in English houses: A cohort study , 2016 .

[8]  Alba Fuertes,et al.  The relationship between quality defects and the thermal performance of buildings , 2018 .

[9]  Nigel Isaacs,et al.  Understanding energy and the non-domestic building stock: in memory of Harry Bruhns , 2014 .

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

[11]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

[12]  Performance and Impact of the Feed-in Tariff Scheme: Review of Evidence , 2015 .

[13]  G. Finnveden,et al.  Scenario types and techniques: Towards a user's guide , 2006 .

[14]  L. D. Shorrock,et al.  The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock , 1997 .

[15]  Sandhya Patidar,et al.  Stochastic modelling techniques for generating synthetic energy demand profiles , 2016 .

[16]  A. Chong,et al.  Uncertainty Analysis in Building Energy Simulation. A Practical Approach , 2015, Building Simulation Conference Proceedings.

[17]  Adam Hawkes,et al.  Energy systems modeling for twenty-first century energy challenges , 2014 .

[18]  Alex Summerfield,et al.  Energy efficiency in the British housing stock: Energy demand and the Homes Energy Efficiency Database , 2013 .

[19]  Vítor Leal,et al.  The relevance of the energy resource dynamics in the mid/long-term energy planning models , 2011 .

[20]  Leslie G. Fishbone,et al.  Markal, a linear‐programming model for energy systems analysis: Technical description of the bnl version , 1981 .

[21]  Will McDowall,et al.  Energy scenario choices: Insights from a retrospective review of UK energy futures , 2016 .

[22]  Pranab J. Baruah,et al.  Uncertainties in future energy demand in UK residential heating , 2015 .

[23]  S. Iniyan,et al.  A review of energy models , 2006 .

[24]  M. Haller,et al.  Fluctuating renewables in a long-term climate change mitigation strategy , 2011 .

[25]  I. Hamilton,et al.  Back to reality: How domestic energy efficiency policies in four European countries can be improved by using empirical data instead of normative calculation. , 2013 .

[26]  Henk Visscher,et al.  Effectiveness of energy renovations: a reassessment based on actual consumption savings , 2018, Energy Efficiency.

[27]  Frank W. Geels,et al.  Mapping the winds of whole system reconfiguration: Analysing low-carbon transformations across production, distribution and consumption in the UK electricity system (1990–2016) , 2019, Research Policy.

[28]  Brian Ó Gallachóir,et al.  Soft-linking of a power systems model to an energy systems model , 2012 .

[29]  Yusuf Yildiz,et al.  Identification of the building parameters that influence heating and cooling energy loads for apartm , 2011 .

[30]  Júlia Seixas,et al.  Mining households' energy data to disclose fuel poverty: Lessons for Southern Europe , 2018 .

[31]  William D'haeseleer,et al.  Impact of the level of temporal and operational detail in energy-system planning models , 2016 .

[32]  Sandhya Patidar,et al.  Quantifying Change in Buildings in a Future Climate and Their Effect on Energy Systems , 2015 .

[33]  Paul Upham,et al.  Original research articleEnergy model, boundary object and societal lens: 35 years of the MARKAL model in the UK , 2014 .

[34]  Zaid Chalabi,et al.  Assessing uncertainty in housing stock infiltration rates and associated heat loss: English and UK case studies , 2015 .

[35]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[36]  Nilay Shah,et al.  A review of policy analysis purpose and capabilities of electricity system models , 2016 .

[37]  Tony Roskilly,et al.  This Work Is Licensed under a Creative Commons Attribution 4.0 International License Royapoor M, Roskilly T. Building Model Calibration Using Energy and Environmental Data. Energy and Buildings Building Model Calibration Using Energy and Environmental Data Keywords: Model Calibration Measured Energy , 2022 .

[38]  Sandhya Patidar,et al.  Synthesising electrical demand profiles for UK dwellings , 2014 .

[39]  Richard A. Buswell,et al.  The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios , 2016 .

[40]  M. Dicorato,et al.  A regional energy planning methodology including renewable energy sources and environmental constraints , 2003 .

[41]  J.L.M. Hensen.  Energy simulation in building design , 1992 .

[42]  Mika Pantzar,et al.  The Dynamics of Social Practice: Everyday Life and how it Changes , 2012 .

[43]  Tadj Oreszczyn,et al.  Solid-wall U-values: heat flux measurements compared with standard assumptions , 2015 .

[44]  David Jenkins,et al.  Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates , 2011 .