An artificial neural network-based system to estimate the thermal comfort of buildings with energy efficiency

Abstract

Heating Ventilation Air Condition (HVAC) systems consume the majority of energy in a building; it is essential to optimize this energy while improving the thermal comfort of the occupants. The Predict Mean Vote (PMV) model is considered as one of the most efficient models to define the thermal comfort of a structure. In this context this paper proposes a prediction of PMV index using ANN algorithm to classify the real-time thermal comfort states of occupants, which may provide future energy savings by adopting time-varying setpoints where real-time changes in thermal comfort may require less energy. The performance of studied algorithm was tested using several evaluation parameters such as mean square error (MSE) and correlation coefficient (R2). The algorithm studied in this article showed promising results in terms of correlation coefficient R2 and MSE.