Self-learning neurocontroller for maintaining indoor relative humidity

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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 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 handler. 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 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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages1297-1301
Number of pages5
Volume2
Publication statusPublished - 2001
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period7/15/017/19/01

ASJC Scopus subject areas

  • Software

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