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.

Living in Mediterranean cities in the context of climate change: A review

“…Climate-adapted planning is required, spanning from individual homes to entire neighborhoods and cities (e.g., Hatuka & Saaroni, 2014). Significant improvements in planning and construction processes are also vital to ensure indoor thermal comfort, requiring suitable materials, insulation, shading and building layouts informed by research tools and advanced models (e.g., Aparicio-Ruiz et al, 2023). Microclimatic analysis, as conducted by Giannaros et al (2023) in Athens, can inform the development of efficient and reliable heat-health warning systems tailored to the specific characteristics of each city.…”

Preliminary Research on Outdoor Thermal Comfort Evaluation in Severe Cold Regions by Machine Learning

“…To describe the neutral thermal comfort of the human body, scholars typically establish a relationship between selected indicators and the actual thermal sensation vote through regression analysis. Research results often vary in different climates [38][39][40][41], and there are still few studies on thermal comfort evaluation in severe cold areas, which are obviously different from tropical, arid, and temperate climates [42,43].…”

“…In recent years, there has been rapid development in the field of Machine Learning (ML) and Artificial Intelligence (AI). These technologies provide new possibilities for thermal comfort modeling that can adapt to regional and individual differences [41,[43][44][45][46][47]. Outdoor thermal comfort has a complex relationship between the outdoor environment, individual emotion, and other factors.…”