A fuzzy adaptive comfort temperature model with grey predictor for multi-agent control system of smart building

In this paper a fuzzy adaptive comfort temperature (FACT) model has been proposed for the intelligent control of smart buildings. A multi-agent control system is applied for the energy management and building operation. Particle Swarm Optimization (PSO) is applied to optimize the set points based on the comfort zone. Integrating a grey predictor to predict outdoor temperature with the FACT model shows great promise in systematically determining the customer temperature comfort zone for smart buildings. With the application of the FACT model and other intelligent technologies, the multi-agent control system has successfully provided a high-level of temperature comfort with low power consumption to customers in smart building environments. Case studies and corresponding simulation results are presented and discussed in this paper.