Self-learning neurocontroller for maintaining indoor relative humidity

Author

Sigumonrong, A.P. ; Bong, T.Y. ; Fok, S.C. ; Wong, Y.W.

Author_Institution

Div. of Thermal & Fluids Eng., Nanyang Technol. Inst., Singapore

Volume

2

fYear

2001

fDate

2001

Firstpage

1297

Abstract

An air-conditioning system is designed to meet maximum space cooling load. Thus the system´s controller needs parameter adjustment periodically due to changes in the environment and operating conditions. For a constant-air-volume system at system part-load operation the indoor relative humidity may exceed the limit recommended for comfort and health. This paper describes the application of neural networks to develop an intelligent air handling. The purpose is twofold: 1) the controller self-learning capability will substitute conventional parameter adjustment; 2) in addition to controlling the indoor temperature, the controller will also limit the indoor relative humidity. With the designed cost function, the proposed controller is a promising tool to limit the rise in indoor relative humidity in this particular constant-air-volume system

Keywords

air conditioning; humidity control; neurocontrollers; unsupervised learning; air-conditioning; humidity control; indoor relative humidity; intelligent control; neural network; neurocontroller; self-learning; Artificial neural networks; Automatic control; Control systems; Cost function; Humidity control; Intelligent networks; Neural networks; Neurocontrollers; Neurons; Temperature control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939548

Filename

939548