Energy Efficiency

, Volume 10, Issue 2, pp 359–382 | Cite as

Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

  • X. CiprianoEmail author
  • A. Vellido
  • J. Cipriano
  • J. Martí-Herrero
  • S. Danov
Original Article


In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings. The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour.


Building energy use Energy building simulation Clustering analysis Urban energy refurbishment 



International Energy Agency–Energy in Building and Communities


European Union Energy Performance of Buildings Directive


Energy use intensity



Xavier Cipriano is thankful to the Catalan Housing Agency of the Catalan Government for funding the main research, as well as to María Heras, Nuria Cardellach, and Eduard Calderón for their participation in the energy audits and surveys to tenants. We also are thankful to Dr. Nicolas Kosoy for his suggestions in the socio-economic analysis of surveys, as well as the Can Jofresa’s neighbour association for their answers and information about their homes. Jaime Martí-Herrero is thankful to the Prometeo Project of the Secretariat for Higher Education, Science, Technology and Innovation of the Republic of Ecuador that funded part of his work.


  1. American Society of Heating, Refrigerating and Air-Conditioning Engineers. (ASHRAE). (1999). Handbook of HVAC applications. Chapter 38 (pp. 8–9).Google Scholar
  2. Bishop, C. M., Svensén, M., & Williams, C. K. I. (1998). GTM: the Generative Topographic Mapping. Neural Computation, 10(1), 215–234.CrossRefzbMATHGoogle Scholar
  3. Bishop, C. M. (1998). Latent variable models. In Learning in graphical models (pp. 371–403). Netherlands: Springer.CrossRefGoogle Scholar
  4. Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42, 68–80. doi: 10.1016/ Scholar
  5. Chicco, G., Napoli, R., Postolache, P., Scutariu, M., & Toader, C. (2003). Customer characterization options for improving the tariff offer. IEEE Transactions on Power Systems, 18, 381–387. doi: 10.1109/TPWRS.2002.807085.CrossRefGoogle Scholar
  6. Código Técnico de la Edificación (CTE). (1999). Ley 38/1999 de 5 de Noviembre, de Ordenación de la Edificación (LOE). Spain: Ministerio de Industria.Google Scholar
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.zbMATHGoogle Scholar
  8. Cruz, R., & Vellido, A. (2011). Semi-supervised analysis of human brain tumours from partially labelled MRS information, using manifold learning models. International Journal of Neural Systems, 21, 17–29.CrossRefGoogle Scholar
  9. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1–38.MathSciNetzbMATHGoogle Scholar
  10. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings. European Commission (2010).Google Scholar
  11. EnergyPlus Energy Simulation Software (v6), US Department of Energy (2009).
  12. Etchells, T. A., Nebot, A., Vellido, A., Lisboa, P. J. G., & Mugica, F. (2006). Learning what is important: feature selection and rule extraction in a virtual course (Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), Bruges, Belgium, pp. 401–406).Google Scholar
  13. Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Pacific Grove: Brooks/Cole Publishing.Google Scholar
  14. Giampietro, M., Mayumi, K., & Sorman, A. H. (2011). The metabolic pattern of societies: where economists fall short. London: Routledge.Google Scholar
  15. Goldstein, D. B., & Eley. (2014). A classification of building energy performance indices. Energy Efficiency, 7, 353–375. doi: 10.1007/s12053-013-9248-0.CrossRefGoogle Scholar
  16. International Energy Agency Energy Conservation in Buildings and Community Systems Programme, (IEA-CBCS) (2010) Annex 33: energy related environmental impact in buildings.Google Scholar
  17. International Energy Agency Energy Conservation in Buildings and Community Systems Programme, (IEA-CBCS) Annex 53 (2013). Total energy use in buildings: analysis & evaluation methods. Final report of ST-C—statistical analysis and prediction methods, Chapter 10—statistical analysis of total energy use.Google Scholar
  18. Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651–666.CrossRefGoogle Scholar
  19. Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45, 1683–1697. doi: 10.1016/j.buildenv.2010.01.021.CrossRefGoogle Scholar
  20. Kohonen, T. (2001). Self-organizing maps (3rd ed.). Berlin: Springer-Verlag.CrossRefzbMATHGoogle Scholar
  21. Li, C., Hong, T., & Yan, D. (2014). An insight into actual energy use and its drivers in high-performance buildings. Applied Energy, 131, 394–410. 10.1016/j.apenergy.2014.06.032.CrossRefGoogle Scholar
  22. Li, X., Bowers, C. P., & Schnier, T. (2010). Classification of energy consumption in buildings with outlier detection. IEEE Transactions on Industrial Electronics, 57, 3639–3644. doi: 10.1109/TIE.2009.2027926.CrossRefGoogle Scholar
  23. Lopes, L., Hokoi, S., Miura, H., & Shuhei, K. (2005). Energy efficiency and energy savings in Japanese residential buildings—research methodology and surveyed results. Energy and Buildings, 37, 698–706. doi: 10.1016/j.enbuild.2004.09.019.CrossRefGoogle Scholar
  24. Mata, E., Sasic Kalagasidis, A., & Johnsson, F. (2014). Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK. Building and Environment, 81, 270–282. doi: 10.1016/j.buildenv.2014.06.013.CrossRefGoogle Scholar
  25. McLoughlin, F., Duffy, A., Conlon, M., (2012). Analysing domestic electricity smart metering data using self organising maps. Lisbon: CIRED Workshop.Google Scholar
  26. Mooi, E., & Sarstedt, M. (2014). A concise guide to market research: the process, data, and methods using IBM SPSS. Springer Books. ISBN: 978-3-642-53964-0 (Print) 978-3-642-53965-7 (Online)Google Scholar
  27. Murray, S. N., Walsh, B. P., Kelliher, D., & O’Sullivan, D. T. J. (2014). Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms—a case study. Building and Environment, 75, 98–107. doi: 10.1016/j.buildenv.2014.01.011.CrossRefGoogle Scholar
  28. Nakagami, H. (1996). Lifestyle change and energy use in Japan: household equipment and energy consumption. Energy, 21, 1157–1167.CrossRefGoogle Scholar
  29. Oca, S. D., & Hong, T. (2014). A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 82, 726–739. 10.1016/j.buildenv.2014.10.021.CrossRefGoogle Scholar
  30. Oca, S. D., & Hong, T. (2015). Occupancy schedules learning process through a data mining framework. Energy & Buildings, 88, 395–408. 10.1016/j.enbuild.2014.11.065.CrossRefGoogle Scholar
  31. 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. doi: 10.1016/j.enconman.2006.04.011.CrossRefGoogle Scholar
  32. Pedrini, A., Westphal, F. S., & Lamberts, R. (2002). A methodology for building energy modelling and calibration in warm climates. Building and environment, Elsevier, 37(8-9), 903–912.CrossRefGoogle Scholar
  33. Räsänen, T., Ruskanen, J., & Kolehmainen, M. (2008). Reducing energy consumption by using self-organizing maps to create more personalized electricity use information. Applied Energy, 85, 830–840. doi: 10.1016/j.apenergy.2007.10.012.CrossRefGoogle Scholar
  34. Ren, X., Yan, D., & Hong, T. (2015). Data mining of space heating system performance in affordable housing. Building and Environment, 89, 1–13. 10.1016/j.buildenv.2015.02.009.CrossRefGoogle Scholar
  35. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65. doi: 10.1016/0377-0427(87)90125-7.CrossRefzbMATHGoogle Scholar
  36. Salmerón, J. M., Cerezuela, A., & Salmerón, R. (2009). Escala de calificación energética para edificios existentes. Cuadernos de Eficiencia Energética: Publicaciones IDAE, Madrid, Spain.Google Scholar
  37. Swan, L., Ugursal, V. I., & Beausoleil-Morrison, I. (2009). Implementation of a Canadian residential energy end-use model for assessing new technology impacts (pp. 1429–1436). Glasgow: Proceedings of Building Simulation.Google Scholar
  38. Tosi, A., Olier, I., & Vellido, A. (2014). Probability ridges and distortion flows: visualizing multivariate time series using a variational Bayesian manifold learning method. 10th Workshop on Self-Organizing Maps (WSOM 2014). Advances in Intelligent Systems and Computing, 295, 55–64.CrossRefGoogle Scholar
  39. Tosi, A., & Vellido, A. (2013). Robust cartogram visualization of outliers in manifold learning (Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), Bruges, Belgium, pp. 555–560).Google Scholar
  40. Tsekouras, G. J., Kotoulas, P. B., Tsirekis, C. D., Dialynas, E. N., & Hatziargyriou, N. D. (2008). A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers. Electric Power Systems Research, 78, 1494–1510. doi: 10.1016/j.epsr.2008.01.010.CrossRefGoogle Scholar
  41. Ueno, T., Sano, F., Saeki, O., & Tsuji, K. (2006). Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Applied Energy, 83, 166–183. doi: 10.1016/j.apenergy.2005.02.002.CrossRefGoogle Scholar
  42. Vellido, A., Martí, E., Comas, J., Rodríguez-Roda, I., & Sabater, F. (2007). Exploring the ecological status of human altered streams through Generative Topographic Mapping. Environmental Modelling & Software, 22(7), 1053–1065.CrossRefGoogle Scholar
  43. Vellido, A., Martín, J. D., Rossi, F., & Lisboa, P. J. G. (2011). Seeing is believing: the importance of visualization in real-world machine learning applications (Procs. of the 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011), pp. 219–226).Google Scholar
  44. Yang, Z., & Becerik-Gerber, B. (2014). Modeling personalized occupancy profiles for representing long term patterns by using ambient context. Building and Environment, 78, 23–35. doi: 10.1016/j.buildenv.2014.04.003.CrossRefGoogle Scholar
  45. Yu, Z., Fung, B. C. M., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behaviour on building energy consumption. Energy and Buildings, 43, 1409–1417. doi: 10.1016/j.enbuild.2011.02.002.CrossRefGoogle Scholar
  46. Yu, Z., Haghighat, F., Fung, B. C. M., & Yoshino, H. (2010). A decision tree method for building energy demand modelling. Energy and Buildings, 42, 1637–1646. doi: 10.1016/j.enbuild.2010.04.006.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • X. Cipriano
    • 1
    Email author
  • A. Vellido
    • 2
  • J. Cipriano
    • 3
  • J. Martí-Herrero
    • 4
    • 1
  • S. Danov
    • 1
  1. 1.Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Building Energy and Environment Group, Edifici GAIA (TR14)TerrassaSpain
  2. 2.Computer Science, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Campus Nord UPCBarcelonaSpain
  3. 3.Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Building Energy and Environment Group, CIMNE-UdL ClassroomLleidaSpain
  4. 4.PROMETEO Researcher, Instituto Nacional de Eficiencia Energética y Energías Renovables (INER)QuitoEcuador

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