24 Hours in advance forecasting of thermal comfort-discomfort levels during the hot period of the year at representative locations of athens city, greece

Moustris, KP; Ziomas, IC; Paliatsos, AG

dc.contributor.author	Moustris, KP	en
dc.contributor.author	Ziomas, IC	en
dc.contributor.author	Paliatsos, AG	en
dc.date.accessioned	2014-03-01T01:29:29Z
dc.date.available	2014-03-01T01:29:29Z
dc.date.issued	2009	en
dc.identifier.issn	1018-4619	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19279
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-67949089846&partnerID=40&md5=f04036d60feec175f78b9f9a40ee65ae	en
dc.subject	Artificial neural networks	en
dc.subject	Athens	en
dc.subject	Cooling power	en
dc.subject	Discomfort index	en
dc.subject	Greece	en
dc.subject	Prediction	en
dc.subject.classification	Environmental Sciences	en
dc.subject.other	artificial neural network	en
dc.subject.other	climate prediction	en
dc.subject.other	cooling	en
dc.subject.other	high temperature	en
dc.subject.other	microclimate	en
dc.subject.other	nature-society relations	en
dc.subject.other	public health	en
dc.subject.other	relative humidity	en
dc.subject.other	urban climate	en
dc.subject.other	urbanization	en
dc.subject.other	weather forecasting	en
dc.subject.other	weather station	en
dc.subject.other	wind velocity	en
dc.subject.other	Athens [Attica]	en
dc.subject.other	Attica	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Southern Europe	en
dc.title	24 Hours in advance forecasting of thermal comfort-discomfort levels during the hot period of the year at representative locations of athens city, greece	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	The growth of the city of Athens in the last decades and the phenomenon of urbanisation obviously have led to the creation of a microclimate with explicit effects on human thermal comfort-discomfort. The knowledge of population thermal comfort-discomfort levels, predictable for the next days, is very important for suitable actions in order to protect public health. In this work, an effort has been made to use Artificial Neural Networks (ANNs) for the forecast of the maximum daily value of Thorn's Discomfort Index, and the minimum daily value of an index known as Cooling Power by Siple and Passel, as well as the number of consecutive hours of thermal discomfort due to high temperatures, for the next day. For this aim, the values of air temperature, relative humidity, wind speed, and the corresponding values of two thermal comfort-discomfort indices for the two previous days were used for the daily forecast. Initially, meteorological data, recorded during period 2001-2004 in eight stations of the network of the Greek Ministry of the Environment, Physical Planning and Public Works (GMEPPPW) in the greater Athens area, were statistically treated. Two of these stations, Patission and Thrakomakedones, which presented maximum and minimum temperatures, were selected. For these two stations ANNs were used in order to forecast the values of the above-mentioned thermal comfort-discomfort indices, as well as the number of consecutive thermal discomfort hours during the day, 24 hours predicted before their appearance.	en
heal.publisher	PARLAR SCIENTIFIC PUBLICATIONS (P S P)	en
heal.journalName	Fresenius Environmental Bulletin	en
dc.identifier.isi	ISI:000266898200012	en
dc.identifier.volume	18	en
dc.identifier.issue	5	en
dc.identifier.spage	601	en
dc.identifier.epage	608	en