Reduced-order functional link neural network for HVAC thermal system identification and modeling

The use of computers for direct digital control highlights the trend toward more effective and efficient HVAC control methodologies. Researchers in the HVAC field have stressed the importance of self-learning in building control systems and the integration of optimal control and other advanced techniques into the formulation of such systems. This paper describes a functional link neural network approach to perform the HVAC thermal system identification and modeling. Artificial neural networks are used to emulate the plant dynamics in order to estimate future plant outputs and obtain plant input/output sensitivity information for online neural control adaptation. Methodologies to appropriately reduce the inputs, thus the complexity, of the functional link network in order to speed up the training are presented. This paper also analyzes and compares the performance and complexity between the functional link network and conventional network approaches for the HVAC thermal system identification and modeling.