Cohort Comfort Model

Cohort comfort models leverage the similarities among individuals to improve predictions and resource efficiency in various applications. Research focuses on developing algorithms that effectively group individuals into cohorts based on shared characteristics (e.g., data distributions, personality traits, physiological responses) to build more accurate and robust models with less individual data. This approach is proving valuable in diverse fields, including personalized thermal comfort prediction in buildings and improving the accuracy and efficiency of federated learning in healthcare, by reducing the need for extensive individual data collection and enhancing model generalization.