In the quest to optimize energy efficiency in buildings, researchers have long sought to understand the nuances of human thermal comfort. A groundbreaking review published by Wenjie Song of the University of Nottingham’s Department of Architecture and Built Environment, sheds new light on the critical role of skin temperature in determining thermal sensation and comfort. This comprehensive review, published in the journal ‘Energy and Built Environment’ (translated from Chinese), synthesizes findings from 172 studies, offering a fresh perspective on how skin temperature influences our perception of heat and cold.
The study underscores the importance of both local and mean skin temperatures, a factor often overlooked in previous research. “The most common measurement points for skin temperature are the face and hands,” Song explains. “This is due to their higher thermal sensitivity and the practical ease of measurement.” This finding has significant implications for the design and operation of heating, ventilation, and air conditioning (HVAC) systems, which could be fine-tuned to target these specific areas for greater efficiency.
One of the most compelling discoveries is the linear relationship between mean skin temperature and thermal sensation. However, this relationship is not straightforward and can be influenced by the choice of measurement locations and the number of points measured. “Local heating is less influential than cooling in changing environments,” Song notes, highlighting the complexity of thermal sensation and the need for tailored approaches in different settings.
The review also delves into demographic variations, revealing that age, gender, and regional differences significantly impact thermal sensation. For instance, elderly individuals exhibit decreased temperature sensitivity, particularly to warmth. Gender differences are also pronounced, with females experiencing higher skin temperatures in warmer environments and lower in colder ones. These insights could revolutionize the way buildings are designed and operated, ensuring comfort for a diverse range of occupants while optimizing energy use.
The study explores the use of machine learning (ML) methods, particularly classification tree and support vector machine (SVM) techniques, to predict thermal sensation and comfort. While ML methods are increasingly popular, statistical regression-based approaches and thermo-physiological model-based methods also offer valuable insights. The integration of individual demographic variables into ML models could pave the way for personalized thermal comfort predictions, a game-changer for the energy sector.
So, how might this research shape future developments? The findings suggest a move towards more personalized and adaptive HVAC systems that consider individual differences in thermal sensation. This could lead to significant energy savings and improved occupant comfort. Moreover, the emphasis on local skin temperature measurements could inspire innovative sensor technologies and control strategies, further enhancing energy efficiency.
As the construction industry continues to evolve, understanding the intricate relationship between skin temperature and thermal comfort will be crucial. Song’s review, published in ‘Energy and Built Environment’, provides a solid foundation for future research and practical applications, paving the way for a more comfortable and energy-efficient built environment. The study also highlights the need for more detailed experiments to examine the impact of demographic factors on thermal comfort, a call to action for researchers and industry professionals alike.
