Analysis of Variables Affecting Indoor Thermal Comfort in Mediterranean Climates Using Machine Learning

Abstract

To improve the energy efficiency and performance of buildings, it is essential to understand the factors that influence indoor thermal comfort. Through an extensive analysis of various variables, actions can be developed to enhance the thermal sensation of the occupants, promoting sustainability and economic benefits in conditioning systems. This study identifies eight key variables: indoor air temperature, mean radiant temperature, indoor globe temperature, CO2, age, outdoor temperature, indoor humidity, and the running mean temperature, which are relevant for predicting thermal comfort in Mediterranean office buildings. The proposed methodology effectively analyses the relevance of these variables, using five techniques and two different databases, Mediterranean climate buildings published by ASHRAE and a study conducted in Seville, Spain. The results indicate that the extended database to 21 variables improves the quality of the metrics by 5%, underscoring the importance of a comprehensive approach in the analysis. Among the evaluated techniques, random forest emerges as the most successful, offering superior performance in terms of accuracy and other metrics, and this method is highlighted as a technique that can be used to assist in the design and operation or control of a building’s conditioning system or in tools that recommend adaptive measures to improve thermal comfort.

Keywords

• building environment
• thermal comfort
• HVAC
• machine learning