A Combined Analytic and Case-based Approach to Thermal Comfort Prediction in Buildings

Despite the obvious importance of thermal comfort in the design of indoor environment, it has not been effectively integrated with design decision support tools. The reasons can be attributed in part to an absence of modular and flexible software architecture that facilitates dynamic data transfer between energy performance, lighting simulation, and thermal comfort modules. Research has shown that the mathematical models of thermal comfort sometimes fail to accurately describe or predict thermal comfort in workplace settings even when the values of environmental and personal parameters are known. Thus, there is a critical need to provide a thermal comfort evaluation framework that, in addition to the algorithmic implementation of mathematical thermal comfort prediction models, would make use of the empirical knowledge base accumulated over the last 20 years from field experiments around the world.