Application of functional link neural network to HVAC thermaldynamic system identification
Teeter, J.
Mo-Yuen Chow
Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb 1998
Volume: 45,
Issue: 1
On page(s): 170-176
ISSN: 0278-0046
References Cited: 21
CODEN: ITIED6
INSPEC Accession Number: 5830877
Digital Object Identifier: 10.1109/41.661318
Current Version Published: 2002-08-06
Abstract
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
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