Optimization Control for Dynamic Thermal Comfort in an Intelligent Inhabitation Environment

Comfortable, healthy, and energy-saving indoor environments can be obtained via a dynamic thermal comfort control. Difficulties to design an optimal control system for a dynamic thermal environment arise due to the lack of coordinative control evaluation methods for conflicting comfort and energy-saving indices. An improved multi-objective algorithm based on discrete PSO (Particle Swarm Optimization) is proposed to calculate the optimal values of parameters in the dynamic comfort control system based on users balance between the comfort and energy conservation. No a priori information or physical indoor environment model is needed. Experiment results demonstrate the effectiveness of the proposed control method.