Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions

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Abstract

Residential energy consumptions are determined by the interaction of many factors. Apart from physical characteristics such as climate, heating type, age, and size of the house, occupants’ behavior and socio-economic aspects are critical. Furthermore, the relative impact of the occupants’ characteristics and behavior seems to differ in various investigations confirming the importance of contextual analysis. In this study, different procedures for obtaining occupancy profiles are described and applied with reference to a residential building stock located in Mediterranean climatic conditions (Italy). The heating and domestic hot water (DHW) energy consumptions and indoor comfort conditions of a representative building were determined by introducing different occupant scenarios in dynamic simulations. The occupancy profiles were built by means of data collected at the University of Calabria using surveys, interviews, bills, and statistical elaborations. Considering different modes of use of the dwelling (Regulations, Current-use, and Statistical), in the simulation process, all the inputs of occupancy, ventilation, lighting, DHW, and heating operation were modified. The Regulations occupancy profile produces an underestimation of heating energy consumption. Additionally, primary energy for DHW is strongly affected by the family composition. The effect of the occupants’ preferences on the energy performance of the building was investigated: mainly energy consumptions and internal comfort conditions vary with the set point temperature and the duration of ventilation. The analysis provides reference procedures for obtaining occupancy profiles. Furthermore, the simulation results demonstrate the significant dependence of heating and DWH primary energy consumption on the characteristics and preferences of occupants in the Mediterranean climate.

Keywords

Occupants’ behavior Energy consumption Residential buildings Heating, domestic hot water Thermal comfort 

References

  1. Aerts, D., Minnen, J., Glorieux, I., Wouters, I., & Descamps, F. (2013). Discrete occupancy profiles from time-use data for user behaviour modelling in homes. In: Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, Chambèry pp. 2421–2427.Google Scholar
  2. AICARR. (2005). Miniguida AICARR - Manuale d’ausilio alla progettazione termotecnica. Italian association of air conditioning heating and refrigeration. Padova: Servizi grafici editoriali.Google Scholar
  3. Andersen, R. V., Toftum, J., Andersen, K. K., & Olesen, B. W. (2009). Survey of occupant behaviour and control of indoor environment in Danish dwellings. Energy and Buildings, 41, 11–16. doi:10.1016/j.enbuild.2008.07.004.CrossRefGoogle Scholar
  4. Andersen, R., Fabi, V., Toftum, J., Corgnati, S. P., & Olesen, B. W. (2013). Window opening behaviour modelled from measurements in Danish dwellings. Building and Environment, 69, 101–113. doi:10.1016/j.buildenv.2013.07.005.CrossRefGoogle Scholar
  5. ASHRAE (2002). ANSI/ASHRAE Guideline 14-2002: measurement of energy and demand savings. Atlanta: American Society of Heating, Refrigeration and Air Conditioning Engineers.Google Scholar
  6. Aune, M. (2007). Energy comes home. Energy Policy, 35, 5457–5465. doi:10.1016/j.enpol.2007.05.007.CrossRefGoogle Scholar
  7. Barthelmes, V. M., Becchio, C., & Corgnati, S. P. (2016). Occupant behavior lifestyles in a residential nearly zero energy building: effect on energy use and thermal comfort. Science and Technology for the Built Environment, 22(7), 960–975. doi:10.1080/23744731.2016.1197758.
  8. Bartiaux, F., & Gram-Hanssen, K. (2005). Socio-political factors influencing household electricity consumption: a comparison between Denmark and Belgium (pp. 1313–1325). In: ECEEE Summer Study proceedings. Mandelieu La Napoule..Google Scholar
  9. Chen, S., Yoshino, H., Li, N., Yang, W., Yoshino, H., Levine, M. D., Newhouse, K., & Hinge, A. (2010). Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China. Energy and Buildings, 42, 136–146. doi:10.1016/j.enbuild.2009.07.003.CrossRefGoogle Scholar
  10. Chen, J., Wang, X., & Steemers, K. (2013). A statistical analysis of a residential energy consumption survey study in Hangzhou, China. Energy and Buildings, 66, 193–202. doi:10.1016/j.enbuild.2013.07.045.CrossRefGoogle Scholar
  11. Climate Change Post (2015). http://www.climatechangepost.com/. Accessed 27 May 2015.
  12. D’Oca, S., & Hong, T. (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88, 395–408. doi:10.1016/j.enbuild.2014.11.065.CrossRefGoogle Scholar
  13. D’Oca, S., Fabi, V., Corgnati, S. P., & Andersen, R. K. (2014). Effect of thermostat and window opening occupant behavior models on energy use in homes. Building Simulation, 7, 683–694. doi:10.1007/s12273-014-0191-6.CrossRefGoogle Scholar
  14. DesignBuilder Software Ltd (2015). DesignBuilder version 4.6.0.015. http://www.designbuilder.co.uk/.
  15. Deurinck, M., Saelens, D., & Roels, S. (2012). Assessment of the physical part of the temperature takeback for residential retrofits. Energy and Buildings, 52, 112–121. doi:10.1016/j.enbuild.2012.05.024.CrossRefGoogle Scholar
  16. DOE-2 (1982). DOE-2 engineers manual, version 2.1 A. In: Energy and Environment Division. Building Energy Simulation Group. Lawrence Berkeley Laboratory. doe2.com/download/doe-21e/DOE-2EngineersManualVersion2.1A.pdf. Accessed 4 March 2016.
  17. DPR 412/93 (1993). Regolamento recante norme per la progettazione, l’installazione, l’esercizio e la manutenzione degli impianti termici degli edifici ai fini del contenimento dei consumi di energia, in attuazione dell’art. 4, comma 4, della legge 9 gennaio 1991. Italy.Google Scholar
  18. EnergyPlus (2015). EnergyPlus version 8.4.0. In: The U.S. Department of Energy. https://energyplus.net/. Accessed 5 April 2016.
  19. Engineering & Construction (2010). Acqua calda sanitaria. http://www.engicos.it/acquacaldasanitaria.htm. Accessed 7 May 2015.
  20. EQUA Simulation AB (2014). User manual, IDA indoor climate and energy, version 4.5. Stockholm, Sweden. http://www.equaonline.com/iceuser/pdf/ICE45eng.pdf.
  21. ESRU. (2001). ESP-r windows v 11.8 GCC3. Glasgow: University of Strathclyde.Google Scholar
  22. Fabi, V., Andersen, R. V., Corgnati, S. P., Olesen, B. W., & Filippi, M (2011). Description of occupant behaviour in building energy simulation: state-of-art and concepts for improvements (pp. 14–16). In: 12th Conference of international building performance simulation association. Sidney.Google Scholar
  23. Fabi, V., Andersen, R. V., Corgnati, S., & Olesen, B. W. (2012). Occupants’ window opening behaviour: a literature review of factors influencing occupant behaviour and models. Building and Environment, 58, 188–198. doi:10.1016/j.buildenv.2012.07.009.CrossRefGoogle Scholar
  24. Frontczak, M., & Wargocki, P. (2011). Literature survey on how different factors influence human comfort in indoor environments. Building and Environment, 46, 922–937. doi:10.1016/j.buildenv.2010.10.021.CrossRefGoogle Scholar
  25. Guerra-Santin, O. (2010). Actual energy consumption in dwellings—the effect of energy performance regulations and occupant behaviour. Amsterdam: IOS Press under the imprint Delft University Press.Google Scholar
  26. Guerra-Santin, O. (2011). Behavioural patterns and user profiles related to energy consumption for heating. Energy and Buildings, 43, 2662–2672. doi:10.1016/j.enbuild.2011.06.024.CrossRefGoogle Scholar
  27. Guerra-Santin, O., & Itard, L. (2010). Occupants’ behaviour: determinants and effects on residential heating consumption. Building Research & Information, 38, 318–338. doi:10.1080/09613211003661074.CrossRefGoogle Scholar
  28. HETUS (Harmonised European Time Use Survey) (2007). In: interactive. https://www.h2.scb.se/tus/tus/Default.htm.
  29. Hong, T., Yan, D., Oca, S. D., & Chen, C. (2017). Ten questions concerning occupant behavior in buildings: the big picture. Building and Environment, 114, 518–530. doi:10.1016/j.buildenv.2016.12.006.CrossRefGoogle Scholar
  30. Howard-Reed, C., Wallace, L. A., & Ott, W. R. (2002). The effect of opening windows on air change rates in two homes. Journal of the Air & Waste Management Association, 52, 147–159. doi:10.1080/10473289.2002.10470775.CrossRefGoogle Scholar
  31. IEA (2015). IEA-EBC Annex 66: definition and simulation of occupant behavior in buildings. www.annex66.org.
  32. Istat. National Institute of statistics (2011). In: Italy. http://www.istat.it/it/. Accessed 15 March 2016.
  33. Istat. National Institute of statistics (2014). In: Italy. http://www.istat.it/it/. Accessed 15 March 2016.
  34. Iwashita, G., & Akasaka, H. (1997). The effects of human behavior on natural ventilation rate and indoor air environment in summer—a field study in southern Japan. Energy and Buildings, 25, 195–205.CrossRefGoogle Scholar
  35. Jones, R. V., Fuertes, A., & Lomas, K. J. (2015). The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings. Renewable and Sustainable Energy Reviews, 43, 901–917. doi:10.1016/j.rser.2014.11.084.CrossRefGoogle Scholar
  36. Kane, T., Firth, S. K., Lomas, K. J., Allinson, D., & Irvine, K (2011). Variation of indoor temperatures and heating practices in UK dwellings (pp. 1–10). In: Proceedings of the research students’ conference on “Buildings don’t use energy, people do?”—domestic energy use and CO2 emissions in existing dwellings. Bath.Google Scholar
  37. Lenzen, M., Wier, M., Cohen, C., Hayami, H., Pachauri, S., & Schaeffer, R. (2006). A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan. Energy, 31, 181–207. doi:10.1016/j.energy.2005.01.009.CrossRefGoogle Scholar
  38. Lin, J., & Iyer, M. (2007). Cold or hot wash: technological choices, cultural change, and their impact on clothes-washing energy use in China. Energy Policy, 35, 3046–3052. doi:10.1016/j.enpol.2006.11.001.CrossRefGoogle Scholar
  39. Lindén, A. L., Carlsson-Kanyama, A., & Eriksson, B. (2006). Efficient and inefficient aspects of residential energy behaviour: what are the policy instruments for change? Energy Policy, 34, 1918–1927. doi:10.1016/j.enpol.2005.01.015.CrossRefGoogle Scholar
  40. Martinaitis, V., Zavadskas, E. K., Motuziene, V., & Vilutiene, T. (2015). Importance of occupancy information when simulating energy demand of energy efficient house: a case study. Energy and Buildings, 101, 64–75. doi:10.1016/j.enbuild.2015.04.031.CrossRefGoogle Scholar
  41. de Meester, T., Marique, A.-F., De Herde, A., & Reiter, S. (2013). Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate in the northern part of Europe. Energy and Buildings, 57, 313–323. doi:10.1016/j.enbuild.2012.11.005.CrossRefGoogle Scholar
  42. Mora, D., Carpino, C., & De Simone, M. (2015). Behavioral and physical factors influencing energy building performances in Mediterranean climate. Energy Procedia, 78, 603–608. doi:10.1016/j.egypro.2015.11.033.CrossRefGoogle Scholar
  43. Motuziene, V., & Vilutiene, T. (2013). Modelling the effect of the domestic occupancy profiles on predicted energy demand of the energy efficient house. Procedia Engineering, 57, 798–807. doi:10.1016/j.proeng.2013.04.101.CrossRefGoogle Scholar
  44. Nicol, J. F (2001). Characterising occupant behavior in buildings: towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans (pp. 1073–1078). In: Seventh International IBPSA Conference. Rio de Janeiro.Google Scholar
  45. Ren, X., Yan, D., & Hong, T. (2015). Data mining of space heating system performance in affordable housing. Building and Environment, 89, 1–13. doi:10.1016/j.buildenv.2015.02.009.CrossRefGoogle Scholar
  46. Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40, 1560–1566. doi:10.1016/j.enbuild.2008.02.006.CrossRefGoogle Scholar
  47. Rijal, H. B. B., Tuohy, P., Humphreys, M. A. A., Nicol, J. F. F., Samuel, a., & Clarke, J. (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39, 823–836. doi:10.1016/j.enbuild.2007.02.003.CrossRefGoogle Scholar
  48. Santamouris, M., Kapsis, K., Korres, D., Livada, I., Pavlou, C., & Assimakopoulos, M. N. (2007). On the relation between the energy and social characteristics of the residential sector. Energy and Buildings, 39, 893–905. doi:10.1016/j.enbuild.2006.11.001.CrossRefGoogle Scholar
  49. Sonderegger, R. C. (1978). Movers and stayers: the resident’s contribution to variation across houses in energy consumption for space heating. Energy and Buildings, 1, 313–324.CrossRefGoogle Scholar
  50. Steemers, K., & Yun, G. Y. (2009). Household energy consumption: a study of the role of occupants. Building Research & Information, 37, 625–637. doi:10.1080/09613210903186661.CrossRefGoogle Scholar
  51. Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renewable and Sustainable Energy Reviews, 13, 1819–1835.CrossRefGoogle Scholar
  52. TRNSYS. (2012). TANSY’S 17, transient system simulation program. Madison: Solar energy laboratory - University of Wisconsin.Google Scholar
  53. Tronchin, L., & Fabbri, K. (2008). Energy performance building evaluation in Mediterranean countries: comparison between software simulations and operating rating simulation. Energy and Buildings, 40, 1176–1187. doi:10.1016/j.enbuild.2007.10.012.CrossRefGoogle Scholar
  54. UNI 10349 (1994). Heating and cooling of buildings—climatic data. Italy: Italian Organization for Standardization (UNI).Google Scholar
  55. UNI/TS 11300-1 (2014). Energy performance of buildings. Part 1: evaluation of energy need for space heating and cooling. Italy: Italian Organization for Standardization (UNI).Google Scholar
  56. UNI/TS 11300-2 (2014). Energy performance of buildings. Part 2: evaluation of primary energy need and of system efficiencies for space heating, domestic hot water production, ventilation and lighting for non-residential buildings. Italy: Italian Organization for Standardization (UNI).Google Scholar
  57. Union Oil (2003). Previsioni di domanda energetica e petrolifera italiana, 2003–2015. In: Italy. https://books.google.it/books?id=w9MgnQAACAAJ. Accessed 4 April 2016.
  58. Wallace, L. A., Emmerich, S. J., & Howard-Reed, C. (2002). Continuous measurements of air change rates in an occupied house for 1 year: the effect of temperature, wind, fans, and windows. Journal of Exposure Analysis and Environmental Epidemiology, 12, 296–306.CrossRefGoogle Scholar
  59. Wei, S., Jones, R., & De Wilde, P. (2014). Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 70, 36–44. doi:10.1016/j.enbuild.2013.11.001.CrossRefGoogle Scholar
  60. Wilhite, H., Nakagami, H., Masuda, T., Yamaga, Y., & Haneda, H. (1996). A cross-cultural analysis of household energy use behaviour in Japan and Norway. Energy Policy, 24, 795–803. doi:10.1016/0301-4215(96)00061-4.CrossRefGoogle Scholar
  61. Yan, D., O’brien, W., Hong, T., Feng, X., Gunay, H. B., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 107, 264–278. doi:10.1016/j.enbuild.2015.08.032.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Dafni Mora
    • 1
    • 2
  • Cristina Carpino
    • 2
  • Marilena De Simone
    • 2
  1. 1.Hydraulic and Hydrotechnical Research Center (CIHH)Technological University of PanamaPanama CityPanama
  2. 2.Department of Mechanical, Energy and Management Engineering (DIMEG)University of CalabriaRendeItaly

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