Application of functional link neural network to HVAC thermaldynamic system identification

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks and expert systems. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating and air-conditioning (HVAC) control methodologies. Researchers in the HVAC field have stressed the importance of self-learning in building control systems and have encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. Artificial neural networks can also be 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. This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. Methodologies to reduce inputs of the functional link network to reduce the complexity and speed up the training speed are presented. Analysis and comparison between the functional link network approach and the conventional network approach for the HVAC thermal modeling are also presented