Breaking New Ground in Thermal Comfort: Advancing Building Efficiency with Explainable AI
Researchers are pioneering a cutting-edge approach to indoor thermal comfort using infrared imaging and explainable artificial intelligence (AI). Analyzing facial temperature data, this innovative method accurately predicts occupant comfort levels.
Environmental and physiological parameters were meticulously collected in climatic chamber experiments using data loggers and infrared cameras. Researchers evaluated various machine learning models to identify the most effective approach for predicting thermal comfort.
Key findings highlight the crucial role of facial thermography in model training and the identification of thresholds indicative of thermal discomfort. This research opens avenues for personalized heating and cooling strategies, optimizing energy usage while ensuring individual comfort.
In summary, this breakthrough study marks a significant leap forward in creating energy-efficient and comfortable building environments, leveraging the synergy of infrared imaging and explainable AI.
For more information, use the link below