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KNN and adaptive comfort applied in decision making for HVAC systems

Author

Listed:
  • Pablo Aparicio-Ruiz

    (Universidad de Sevilla)

  • Elena Barbadilla-Martín

    (Universidad de Sevilla)

  • José Guadix

    (Universidad de Sevilla)

  • Pablo Cortés

    (Universidad de Sevilla)

Abstract

The decision making of a suitable heating, ventilating and air conditioning system’s set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being’s thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system.

Suggested Citation

  • Pablo Aparicio-Ruiz & Elena Barbadilla-Martín & José Guadix & Pablo Cortés, 2021. "KNN and adaptive comfort applied in decision making for HVAC systems," Annals of Operations Research, Springer, vol. 303(1), pages 217-231, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-019-03489-4
    DOI: 10.1007/s10479-019-03489-4
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    References listed on IDEAS

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    3. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    4. S. Camelo & M. González-Lima & A. Quiroz, 2015. "Nearest neighbors methods for support vector machines," Annals of Operations Research, Springer, vol. 235(1), pages 85-101, December.
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    Cited by:

    1. Liu, Yang & Pedrycz, Witold & Deveci, Muhammet & Chen, Zhen-Song, 2024. "BIM-based building performance assessment of green buildings - A case study from China," Applied Energy, Elsevier, vol. 373(C).

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