Strategic Sustainable and Smart Development Based on User Behaviour

  • Shahryar Habibi
  • Theo Zaffagnini
Part of the Innovative Renewable Energy book series (INREE)


It is clear that the field of artificial intelligence (AI) as a decision-oriented tool has recently proven to be a viable alternative approach to solve environmental challenges. For example, artificial neural networks (ANNs) and support vector machines (SVMs), which are a subset of artificial intelligence, are going to be widely used to predict energy consumption in the buildings. The work aims to explore the use of user behaviour and smart and passive systems to improve energy efficiency and indoor environmental quality (IEQ) in buildings. The presence of users within buildings can affect process improvement. For example, users can contribute to energy efficiency by switching off artificial lighting during daylight hours. Furthermore, they can reduce the use of energy by changing their behaviour to act according to principles of sustainable development. In order to evaluate the impact of user behaviour on energy consumption, development of an assessment model based on AI can be useful. On the other hand, the use of a new concept from artificial intelligence in assessment tools can not only explore the potential benefits of approach but also provide ways to achieve an optimum level of efficiency.


  1. 1.
    Fabi V, Buso T, Andersen RK, Corgnati SP, Olesen BW (2013) Robustness of building design with respect to energy related occupant behavior. In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, 1999–2006Google Scholar
  2. 2.
    Brager G, de Dear R (1998) Thermal adaptation in the built environment: a literature review. Energ Buildings 27:83–96CrossRefGoogle Scholar
  3. 3.
    Cena K, de Dear R (2001) Thermal comfort and behavioural strategies in office buildings located in a hot-arid climate. J Therm Biol 26:409–414CrossRefGoogle Scholar
  4. 4.
    Parsons KC (2002) The effects of gender, acclimation state, the opportunity to adjust clothing and physical disability on requirements for thermal comfort. Energ Buildings 34:593–599CrossRefGoogle Scholar
  5. 5.
    van Hoof J, Mazej M, Hensen JLM (2010) Thermal comfort: research and practice. Front Biosci 15:765–788CrossRefGoogle Scholar
  6. 6.
    Liu J, Yao R, Wang J, Li B (2011) Occupants’ behavioural adaptation in workplaces with non-central heating and cooling systems. Appl Therm Eng 35:40–54CrossRefGoogle Scholar
  7. 7.
    Brittle JP, Eftekhari M, Firth SK (2016) Mechanical ventilation & cooling energy versus thermal comfort: a study of mixed mode office building performance in Abu Dhabi. In: Brotas L, Roaf S, Nicol F, Humphreys M (eds) Proceedings of the 9th Windsor conference. NCEUB: making comfort relevant, 7–10th April 2016, WindsorGoogle Scholar
  8. 8.
    Lian Z, Zhao B, Liu W (2007) A neural network evaluation model for individual thermal comfort. Energ Buildings 39:1115–1122CrossRefGoogle Scholar
  9. 9.
    UC Berkeley Center for the Built Environment (2014) [Online; Accessed 23 May 2014]
  10. 10.
    Nest Company (2014) [Online; Accessed 10 May 2014]
  11. 11.
    Frontczak M, Schiavon S, Goins J, Arens E, Zhang H, Wargocki P (2012) Quantitative relationships between occupant satisfaction and aspects of indoor environmental quality and building design. Indoor Air Journal 22:119–113CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ArchitectureUniversity of FerraraFerraraItaly

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