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Building Simulation

, Volume 11, Issue 4, pp 725–737 | Cite as

Energy evaluation of residential buildings: Performance gap analysis incorporating uncertainties in the evaluation methods

  • Ingrid Allard
  • Thomas Olofsson
  • Gireesh Nair
Research Article Building Thermal, Lighting, and Acoustics Modeling

Abstract

Calculation and measurement-based energy performance evaluations of the same building often provide different results. This difference is referred as “the performance gap”. However, a large performance gap may not necessarily mean that there are flaws in the building or deviations from the intended design. The causes for the performance gap can be analysed by calibrating the simulation model to measured data. In this paper, an approach is introduced for verifying compliance with energy performance criteria of residential buildings. The approach is based on a performance gap analysis that takes the uncertainties in the energy evaluation methods into consideration. The scope is to verify building energy performance through simulation and analysis of measured data, identifying any performance gap due to deviations from the intended design or flaws in the finished building based on performance gap analysis. In the approach, a simulation model is calibrated to match the heat loss coefficient of the building envelope [kWh/K] instead of the measured energy. The introduced approach is illustrated using a single-family residential building. The heat loss coefficient was found useful towards identifying any deviations from the intended design or flaws in the finished building. The case study indicated that the method uncertainty was important to consider in the performance gap analysis and that the proposed approach is applicable even when the performance gap appears to be non-existing.

Keywords

performance gap energy signature calibration simulation design criteria 

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Notes

Acknowledgements

This study was partly conducted during the project SBHN— Sustainable Buildings for the High North— supported by the European Neighbourhood and Partnership Instrument of the European Union under the Kolarctic ENPI CBC programme. The authors would like to thank Mark Murphy, Umeå University, Department of Applied Physics and Electronics, for his assistance with the simulation program IDA ICE.

References

  1. Allard I, Olofsson T, Nair G (2017). Energy Performance Indicators in the Swedish Building Procurement Process. Sustainability, 9: 1877.CrossRefGoogle Scholar
  2. ASHRAE (2015}). ASHRAE Handbook—Fundamentarls. Atlanta: American Society of Heating, Refridgeratiing and Air-conditioning EngineersGoogle Scholar
  3. Berg F, Flyen A-C, Lund Godbolt Å, Broström T (2017). User-driven energy efficiency in historic buildings: A review. Journal of Cultural Heritage, 28: 188–195.CrossRefGoogle Scholar
  4. Bülow-Hübe H (2001). Energy-Efficient Window Systems—Effects on Energy Use and Daylight in Buildings. Lund: KFS AB.Google Scholar
  5. Burman E, Mumovic D, Kimpian J (2014). Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings. Energy, 77: 153–163.CrossRefGoogle Scholar
  6. Coakley D, Raftery P, Keane M (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews, 37: 123–141.CrossRefGoogle Scholar
  7. Danielski I (2012). Large variations in specific final energy use in Swedish apartment buildings: Causes and solutions. Energy and Buildings, 49: 276–285.CrossRefGoogle Scholar
  8. de Wilde P (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41: 40–49.CrossRefGoogle Scholar
  9. El Fouih Y, Stabat P, Rivière P, Hoang P, Archambault V (2012). Adequacy of air-to-air heat recovery ventilation system applied in low energy buildings. Energy and Buildings, 54: 29–39.CrossRefGoogle Scholar
  10. EQUA Simulation AB (2013). User Manual—IDA Indoor Climate and Energy, Version 4.5. Solna, Sweden: EQUA Simulation AB.Google Scholar
  11. EQUA Simulation AB (2017). IDA Indoor Climate and Energy. Available at http://www.equa.se/en/ida-ice. Accessed 20 Jan 2017.Google Scholar
  12. Gram-Hanssen K, Georg S (2017). Energy performance gaps: promises, people, practices. Building Research & Information, 46: 1–9.CrossRefGoogle Scholar
  13. Hammarsten S (1987). A critical appraisal of energy-signature models. Applied Energy, 26: 97–110.CrossRefGoogle Scholar
  14. Ioannou A, Itard LCM (2015). Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy. Energy and Buildings, 96: 216–233.CrossRefGoogle Scholar
  15. Institute for Energy and Transport. (2012). Photovoltaic Geographical Information System (PVGIS). European Comission. Available at http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php. Accessed 26 Sept 2017.Google Scholar
  16. ISO (2017). Thermal performance of buildings—Transmission and ventilation heat transfer coefficients —Calculation method. Geneva: International Organization for Standardization.Google Scholar
  17. Lidelöw S, Flodberg Munck K (2015). Byggentreprenörens energisignatur. Luleå och Malmö: NCC Construction Sverige och Luleå tekniska universitet. (in Swedish)Google Scholar
  18. Mahdavi A (2011). The human dimension of building performance simulation. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.Google Scholar
  19. Maile T, Bazjanac V, Fischer M (2012). A method to compare simulated and measured data to assess building energy performance. Building and Environment, 56: 241–251.CrossRefGoogle Scholar
  20. Meteotest (2014). Meteonorm 7.1. Available at http://meteonorm.com. Accessed 10 Jan 2014.Google Scholar
  21. Östin R, Eklund E, Johansson C (2012). Energieffektivt byggande i kallt klimat. CERBOF. (in Swedish)Google Scholar
  22. Reddy T (2006). Literature review on calibration of building energy simulation programs: Uses, problems, procedures, uncertainty, and tools. ASHRAE Transactions, 112(1): 226–240.Google Scholar
  23. Reddy T, Maor I, Panjapornpon C (2007). Calibrating detailed building energy simulation programs with measured data—Part 1: General methodology (RP-1051). HVAC&R Research, 13: 221–241.CrossRefGoogle Scholar
  24. Sahlin P, Bring A (1991). IDA Solver—A tool for building and energy systems simulation. In: Proceedings of International IBPSA Building Simulation Conference (pp. 339–348), Nice, France.Google Scholar
  25. Schild P, Mysen M (2009). Technical Note AIVC 65—Recommendations on Specific Fan Power and Fan System Efficiency. Energy Conservation in Buildings and Community Systems Programme, International Energy Agency.Google Scholar
  26. Schultz L (2003). Normalårskorrigering av energianvändningen i byggnader - en jämförelse mellan två metoder. Effektiv. (in Swedish)Google Scholar
  27. Sjögren J-U, Andersson S, Olofsson T (2009). Sensitivity of the total heat loss coefficient determined by the energy signature approach to different time periods and gained energy. Energy and Buildings, 41: 801–808.CrossRefGoogle Scholar
  28. The Swedish National Board of Housing (2012). Handbok för energihushållning enligt Boverkets byggregler - utgåva två. the Swedish National Board of Housing, Building, and Planning.Google Scholar
  29. The Swedish National Board of Housing, Building and Planning (2007). Indata för energiberäkningar i kontor och småhus. En sammanställning av brukarrelaterad indata för elanvändning, personvärme och tappvarmvatten. The Swedish National Board of Housing, Building and Planning (Boverket).Google Scholar
  30. The Swedish National Board of Housing, Building and Planning (2015). BBR 22 - Boverkets föreskrifter om ändring i verkets byggregler (2011:6) - föreskrifter och allmänna råd. The Swedish National Board of Housing Building and Planning (Boverket).Google Scholar
  31. Torcellini P, Pless S, Deru M, Crawley D (2006). Zero energy buildings: A critical look at the definition. ACEEE Summer Study. Pacific Grove, CA, USA: National Renewable Energy Laboratory.Google Scholar
  32. Vesterberg J, Andersson S, Olofsson T (2016). A single-variate building energy signature approach for periods with substantial solar gain. Energy and Buildings, 122: 185–191.CrossRefGoogle Scholar
  33. Wang S, Yan C, Xiao F (2012). Quantitative energy performance assessment methods for existing buildings. Energy and Buildings, 55: 873–888.CrossRefGoogle Scholar
  34. Yousefi F, Gholipour Y, Yan W (2017). A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’ data. Energy and Buildings, 148: 182–198.CrossRefGoogle Scholar
  35. Zhao H-x, Magoulès F (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16: 3586–3592.CrossRefGoogle Scholar
  36. Zhao M, Künzel HG, Antretter F (2015). Parameters influencing the energy performance of residential buildings in different Chinese climate zones. Energy and Buildings, 96: 64–75.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Physics and ElectronicsUmeå UniversityUmeåSweden

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