Self-learning neurocontroller for maintaining indoor relative humidity

被引：0

作者：

Sigumonrong, AP ^{[1
]}

Bong, TY ^{[1
]}

Wong, YW ^{[1
]}

Fok, SC ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Mech & Prod Engn, Div Thermal & Fluids Engn, Singapore 639798, Singapore

来源：

PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INDOOR AIR QUALITY, VENTILATION AND ENERGY CONSERVATION IN BUILDINGS, VOLS I-III | 2001年

关键词：

D O I：

暂无

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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 handier. 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.

引用

页码：1935 / 1941

页数：7