Enhancing Comfort of Occupants in Energy Buildings

  • Monalisa Pal
  • Amr Alzouhri Alyafi
  • Sanghamitra Bandyopadhyay
  • Stéphane Ploix
  • Patrick Reignier
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 225)


As buildings contribute significantly towards global energy consumption, it is essential that the occupants receive the best comfort without utilizing further energy. This work treats building, environment and the occupants as a system, which presents the context, and the occupants also provide their comfort criteria to a black box for yielding the schedule of actions (opening/closing of doors/windows) for optimal comfort. The physical state of an office, situated in France, is recorded over a span of 100 days. This data is utilized by a physical model of the building to simulate the indoor ambience based on random sets of user actions from which an optimal schedule is obtained, representing equally best trade-off among minimal thermal and CO\(_2\)-based air quality dissatisfaction. Results indicate that adopting the proposed schedule of user actions can efficiently enhance the occupant’s comfort.


Differential evolution Energy management Multi-objective optimization Pareto optimality Smart buildings 



This study has been supported by the Indian side of the project sanctioned vide DST-INRIA/2015-02/BIDEE/0978 by the Indo-French Centre for the Promotion of Advanced Research (CEFIPRA—IFCPAR).

This work benefits from the support of the INVOLVED ANR-14-CE22-0020-01 project ( of the French National Research Agency, ANR: Agence Nationale de la recherche, which aims at implementing new occupant interactive energy services, like MIRROR, WHAT-IF and SUGGEST, into a positive energy building constructed at Strasbourg in France by Elithis.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Monalisa Pal
    • 1
  • Amr Alzouhri Alyafi
    • 2
    • 3
  • Sanghamitra Bandyopadhyay
    • 1
  • Stéphane Ploix
    • 2
  • Patrick Reignier
    • 4
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.GSCOP LaboratoryGrenoble Institute of TechnologyGrenobleFrance
  3. 3.LIG LaboratoryGrenoble Institute of TechnologyGrenobleFrance
  4. 4.University of Grenoble Alpes, CNRS, INRIA, LIGGrenobleFrance

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