Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece)

dc.contributor.author	Moustris, KP	en
dc.contributor.author	Tsiros, IX	en
dc.contributor.author	Ziomas, IC	en
dc.contributor.author	Paliatsos, AG	en
dc.date.accessioned	2014-06-06T06:50:02Z
dc.date.available	2014-06-06T06:50:02Z
dc.date.issued	2010	en
dc.identifier.issn	10934529	en
dc.identifier.uri	http://dx.doi.org/10.1080/10934520903540554	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/4946
dc.subject	Artificial neural networks	en
dc.subject	Bioclimatology	en
dc.subject	Environmental management	en
dc.subject	Estimation	en
dc.subject	Microclimate	en
dc.subject	Prediction	en
dc.subject	Thermal monitoring	en
dc.subject	Thermal sensation	en
dc.subject	Urban climate	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Bioclimatology	en
dc.subject.other	Thermal monitoring	en
dc.subject.other	Thermal sensations	en
dc.subject.other	Urban climates	en
dc.subject.other	Atmospheric humidity	en
dc.subject.other	Climatology	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Environmental management	en
dc.subject.other	Thermal comfort	en
dc.subject.other	Weather forecasting	en
dc.subject.other	Neural networks	en
dc.subject.other	air temperature	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	cooling power index	en
dc.subject.other	discomfort index	en
dc.subject.other	forecasting	en
dc.subject.other	Greece	en
dc.subject.other	humidity	en
dc.subject.other	meteorology	en
dc.subject.other	microclimate	en
dc.subject.other	model	en
dc.subject.other	risk	en
dc.subject.other	scoring system	en
dc.subject.other	season	en
dc.subject.other	temperature sense	en
dc.subject.other	thermal comfort	en
dc.subject.other	urban area	en
dc.subject.other	velocity	en
dc.subject.other	wind	en
dc.subject.other	Body Temperature Regulation	en
dc.subject.other	Climate	en
dc.subject.other	Forecasting	en
dc.subject.other	Greece	en
dc.subject.other	Humans	en
dc.subject.other	Neural Networks (Computer)	en
dc.title	Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece)	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/10934520903540554	en
heal.publicationDate	2010	en
heal.abstract	The present study deals with the development and application of Artificial Neural Network (ANN) models as a tool for the evaluation of human thermal comfort conditions in the urban environment. ANNs are applied to forecast for three consecutive days during the hot period of the year (May-September) the human thermal comfort conditions as well as the daily number of consecutive hours with high levels of thermal discomfort in the great area of Athens (Greece). Modeling was based on bioclimatic data calculated by two widely used biometereorogical indices (the Discomfort Index and the Cooling Power Index) and microclimatic data (air temperature, relative humidity and wind speed) from 7 different meteorological stations for the period 2001-2005. Model performance showed that the risk of human discomfort conditions exceeding certain thresholds can be successfully forecasted by the ANN models. In addition, despite the limitations of the models, the results of the study demonstrated that ANNs, when adequately trained, could have a high applicability in the area of prevention human thermal discomfort levels in urban areas, based on a series of relatively limited number of bioclimatic data values calculated prior to the period of interest. Copyright © Taylor & Francis Group, LLC.	en
heal.journalName	Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering	en
dc.identifier.issue	4	en
dc.identifier.volume	45	en
dc.identifier.doi	10.1080/10934520903540554	en
dc.identifier.spage	447	en
dc.identifier.epage	453	en