A Hybrid Regression System Based on Local Models for Solar Energy Prediction

Título

dc.contributor.author	Quintián Pardo, Héctor
dc.contributor.author	Calvo Rolle, José L.
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.date.accessioned	2017-09-05T10:59:37Z
dc.date.available	2017-09-05T10:59:37Z
dc.date.issued	2014
dc.identifier.citation	Informática. Volumen 25 (2), pp. 265-282.
dc.identifier.issn	0868-4952
dc.identifier.uri	http://hdl.handle.net/10366/134316
dc.description.abstract	The aim of this study is to predict the energy generated by a solar thermal system. To achieve this, a hybrid intelligent system was developed based on local regression models with low complexity and high accuracy. Input data is divided into clusters by using a Self Organization Maps; a local model will then be created for each cluster. Different regression techniques were tested and the best one was chosen. The novel hybrid regression system based on local models is empirically verified with a real dataset obtained by the solar thermal system of a bioclimatic house.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	A Hybrid Regression System Based on Local Models for Solar Energy Prediction
dc.type	info:eu-repo/semantics/article
dc.rights.accessRights	info:eu-repo/semantics/openAccess