Supervisory control of a building heating system based on radial basis function neural networks

The building sector is considered a major consumer of energy among all economic sectors. It represents approximately 40 percent of energy consumption. An important energy consumer is associated with building heating systems. The main objective of this paper is to use a novel supervisory control strategy based on RBF neural networks architecture to achieve a good temperature regulation in a building zone and to insure operational safety in the presence of different types of disturbances such as neighborhood temperatures, weather data. The simulation results for different situations show the importance and the effectiveness of this control methodology for unknown nonlinear dynamical processes.