Multivariable control of single zone hydronic heating systems with neural networks

Kanarachos, A; Geramanis, Ke platform networks; Satellite networks; Spectrum planningral networks

dc.contributor.author	Kanarachos, A	en
dc.contributor.author	Geramanis, K	en
dc.date.accessioned	2014-03-01T01:13:55Z
dc.date.available	2014-03-01T01:13:55Z
dc.date.issued	1998	en
dc.identifier.issn	0196-8904	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12788
dc.subject	Direct temperature control	en
dc.subject	Energy conservation	en
dc.subject	Hydronic	en
dc.subject	Neural networks	en
dc.subject.classification	Thermodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Mechanics	en
dc.subject.classification	Physics, Nuclear	en
dc.title	Multivariable control of single zone hydronic heating systems with neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0196-8904(98)00015-6	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0196-8904(98)00015-6	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	In a hydronic heating system, it is important to control the zone temperature with a certain degree of accuracy. The system consists of a space heating circuit with the ability to modulate both the fuel mass flow of the burner and the mass flow of hot water. The physical model of the system consists of a heating system, a distribution system and an environmental zone which are simulated by an eleventh order nonlinear system. The objective of the controller is to maintain the zone temperature as close as possible to a chosen setpoint and, at the same time, to maintain the temperature of the hot water below a maximum temperature which is usually 90 degrees C. However, there are many problems in designing such a controller due to system uncertainties, saturation of the actuators and long system delay. To overcome the above nonlinearities, an adaptive controller has been designed using neural networks and evaluated with software simulations. Comparisons are made between the RMS of the zone temperature and the energy consumption of the system for a time period of two days by several neural networks with different architectures and learning laws. (C) 1998 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Energy Conversion and Management	en
dc.identifier.doi	10.1016/S0196-8904(98)00015-6	en
dc.identifier.isi	ISI:000074500900002	en
dc.identifier.volume	39	en
dc.identifier.issue	13	en
dc.identifier.spage	1317	en
dc.identifier.epage	1336	en