KNN and adaptive comfort applied in decision making for HVAC systems

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.

Automated summary

This paper proposes a general framework to define indoor temperature based on a dynamic adaptive comfort algorithm. By learning the comfortable temperature with respect to running mean temperature, the method can determine the suitable indoor temperature range. The K-Nearest-Neighbour algorithm is shown to represent thermal comfort data patterns better than traditional solutions and is suitable for learning the thermal comfort area of a building and defining set-point temperature for HVAC systems.