Building Simulation

, Volume 10, Issue 1, pp 11–24 | Cite as

Relationship between built form and energy performance of office buildings in a severe cold Chinese region

  • Wei Tian
  • Song Yang
  • Jian Zuo
  • ZhanYong Li
  • YunLiang Liu
Research Article Building Thermal, Lighting, and Acoustics Modeling


It is well recognized that building form has significant influences on energy performance in buildings, especially in the cold climate. It is imperative to understand the relationship between built forms and energy use in order to provide guidance in early project stage such as preliminary design. Therefore, this study focuses on two aspects to understand characteristics of energy use due to the change of parameters related to building form. The first aspect is to apply new metamodel global sensitivity analysis to determine key factors influencing energy use and the second aspect is to develop reliable fast-computing statistical models using state-of-art machine learning methods. An office building, located in Harbin, China, is chosen as a case study using EnergyPlus simulation program. The results indicate that non-linear relationships exist between input variables and energy use for both heating and electricity use. For heating energy, two factors (floor numbers and building scale) show a non-linear yet monotonic trend. For electricity use intensity, building scale is the only significant factor that has non-linear effects. It is also found that the ranking results of critical factors to both electricity use and heating energy per floor area vary significantly between small and large scale buildings. Neural network model performs better than other machine-learning methods, including ordinary linear model, MARS (multivariate adaptive regression splines), bagging MARS, support vector machine, random forest, and Gaussian process.


built form energy performance simulation model sensitivity analysis machine learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. AlAnzi A, Seo D, Krarti M (2009). Impact of building shape on thermal performance of office buildings in Kuwait, Energy Conversion and Management, 50: 822–828.CrossRefGoogle Scholar
  2. ASHRAE (2013). Handbook of Fundamentals. Atlanta: American Society of Heating, Air-Conditioning and Refrigeration EngineersGoogle Scholar
  3. Badescu V (2013). Assessing the performance of solar radiation computing models and model selection procedures, Journal of Atmospheric and Solar-Terrestrial Physics, 105: 119–134.CrossRefGoogle Scholar
  4. Berardi U (2016). A cross-country comparison of the building energy consumptions and their trends. Resources, Conservation and Recycling, doi: 10.1016/j.resconrec.2016.1003.1014.Google Scholar
  5. Catalina T, Virgone J, Iordache V (2011). Study on the impact of the building form on the energy consumption. In: Proceedings of International IBPSA Building Simulation Conference, Sydney, Australia.Google Scholar
  6. de Wilde P, Tian W (2009). Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change, Building Simulation, 2: 157–174.CrossRefGoogle Scholar
  7. DOE (2015). EnergyPlus V8.3. Department of Energy, USA.Google Scholar
  8. Gilg GJ, Valentine FL (2008). Building Geometry: Energy Use Effect. In: Anwar S, Capehart BL (eds), Encyclopedia of Energy Engineering and Technology. Boca Raton, FL, USA: Taylor & Francis, 111–115.Google Scholar
  9. Gramacy RB, Taddy MA (2012). Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models, Journal of Statistical Software, 33(6): 1–48.Google Scholar
  10. Hastie T, Tibshirani R, Friedman J (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.CrossRefMATHGoogle Scholar
  11. Kollas C, Kersebaum KC, Nendel C, et al. (2015). Crop rotation modelling—A European model intercomparison, European Journal of Agronomy, 70: 98–111.CrossRefGoogle Scholar
  12. Kuhn M, Johnson K (2013). Applied Predictive Modeling. New York: Springer.CrossRefMATHGoogle Scholar
  13. Loeppky JL, Sacks J, Welch WJ (2009). Choosing the sample size of a computer experiment: A practical guide, Technometrics, 51: 366–376.MathSciNetCrossRefGoogle Scholar
  14. Mara TA, Tarantola S (2008). Application of global sensitivity analysis of model output to building thermal simulations, Building Simulation, 1: 290–302.CrossRefGoogle Scholar
  15. McKeen P, Fung A (2014). The effect of building aspect ratio on energy efficiency: A case study for multi-unit residential buildings in Canada, Buildings, 4: 336–354.CrossRefGoogle Scholar
  16. MOC (2005). GB50189-2005, Energy Conservation Design Regulation for Public Buildings. Ministry of Construction (MOC) of China. Beijing: China Planning Press. (in Chinese)Google Scholar
  17. MOC (2015). GB50189-2015, Design standard for energy efficiency of public buildings. Ministry of Construction (MOC) of China. Beijing: China Planning Press. (in Chinese)Google Scholar
  18. Mottahedi M, Mohammadpour A, Amiri SS, Riley D, Asadi S (2015). Multi-linear regression models to predict the annual energy consumption of an office building with different shapes, Procedia Engineering, 118: 622–629.CrossRefGoogle Scholar
  19. Nguyen A-T, Reiter S (2015). A performance comparison of sensitivity analysis methods for building energy models, Building Simulation, 8: 651–664.CrossRefGoogle Scholar
  20. Ourghi R, Al-Anzi A, Krarti M (2007). A simplified analysis method to predict the impact of shape on annual energy use for office buildings, Energy Conversion and Management, 48: 300–305.CrossRefGoogle Scholar
  21. Parasonis J, Keizikas A, Kalibatiene D (2012). The relationship between the shape of a building and its energy performance, Architectural Engineering and Design Management, 8: 246–256.CrossRefGoogle Scholar
  22. R Development Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
  23. Saltelli A (2002). Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, 145: 280–297.CrossRefMATHGoogle Scholar
  24. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008). Global Sensitivity Analysis: The Primer. Chichester, UK: Jonh Wiley & Sons.MATHGoogle Scholar
  25. Song J, Wei L, Sun Y, Tian W (2014). Implementation of metamodelling for sensitivity analysis in building energy analysis. In: Proceedings of eSim 2014, Ottawa, Canada.Google Scholar
  26. Steadman P, Hamilton I, Evans S (2014). Energy and urban built form: An empirical and statistical approach, Building Research & Information, 42: 17–31.CrossRefGoogle Scholar
  27. THUBERC (2016). 2016 Annual Report on China Building Energy Efficiency, THUBERC, Building Energy conservation Research Center. Beijing: China Architecture & Building Press. (in Chinese)Google Scholar
  28. Tian W (2013). A review of sensitivity analysis methods in building energy analysis, Renewable and Sustainable Energy Reviews, 20: 411–419.CrossRefGoogle Scholar
  29. Tian W, Choudhary R, Augenbroe G, Lee SH (2015). Importance analysis and meta-model construction with correlated variables in evaluation of thermal performance of campus buildings, Building and Environment, 92: 61–74.CrossRefGoogle Scholar
  30. Tian W, Yang S, Li Z, Lee SH (2016). Identifying informative energy data in Bayesian calibration of building energy models, Energy and Buildings 119: 363–376.CrossRefGoogle Scholar
  31. DCLG (2011). Zero carbon non-domestic buildings. Phase 3 Final Report, Department for Communities and Local Government, UK.Google Scholar
  32. Wei L, Tian W, Zuo J, Yang Z Y, Liu Y, Yang S (2016). Effects of building form on energy use for buildings in cold climate regions, Procedia Engineering, 146: 182–189.CrossRefGoogle Scholar
  33. Xia C, Zhu Y, Lin B (2008). Building simulation as assistance in the conceptual design, Building Simulation, 1: 46–52.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Wei Tian
    • 1
  • Song Yang
    • 1
  • Jian Zuo
    • 2
  • ZhanYong Li
    • 1
  • YunLiang Liu
    • 1
  1. 1.Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical EngineeringTianjin University of Science and TechnologyTianjinChina
  2. 2.School of Architecture & Built Environment; Entrepreneurship, Commercialisation and Innovation Centre (ECIC)University of AdelaideAdelaideAustralia

Personalised recommendations