Title :
Reduced-order functional link neural network for HVAC thermal system identification and modeling
Author :
Chow, Mo-Yuen ; Teeter, Jason
Author_Institution :
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA
Abstract :
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
Keywords :
HVAC; building management systems; identification; intelligent control; neurocontrollers; optimal control; reduced order systems; HVAC thermal system; building control systems; online neural control adaptation; optimal control; plant dynamics; reduced-order functional link neural network; self-learning; Artificial neural networks; Control systems; Digital control; Intelligent control; Neural networks; Optimal control; Performance analysis; Space heating; System identification; Temperature;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.611625