A hybrid non-linear regression midterm energy forecasting method using data mining

Tsekouras, GJ; Elias, ChN; Kavatza, S; Contaxis, GC

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Elias, ChN	en
dc.contributor.author	Kavatza, S	en
dc.contributor.author	Contaxis, GC	en
dc.date.accessioned	2014-03-01T02:42:11Z
dc.date.available	2014-03-01T02:42:11Z
dc.date.issued	2003	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30840
dc.subject	Data mining	en
dc.subject	Energy forecasting	en
dc.subject	Hybrid non-linear multivariable regression model	en
dc.subject.other	Correlation analysis	en
dc.subject.other	Energy forecasting	en
dc.subject.other	High voltage	en
dc.subject.other	Multivariable regression model	en
dc.subject.other	Non-linear regression	en
dc.subject.other	Regression method	en
dc.subject.other	Residential customers	en
dc.subject.other	Statistical indices	en
dc.subject.other	Weather parameters	en
dc.subject.other	Energy utilization	en
dc.subject.other	Forecasting	en
dc.subject.other	Data mining	en
dc.title	A hybrid non-linear regression midterm energy forecasting method using data mining	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/PTC.2003.1304161	en
heal.identifier.secondary	http://dx.doi.org/10.1109/PTC.2003.1304161	en
heal.identifier.secondary	1304161	en
heal.publicationDate	2003	en
heal.abstract	The objective of this paper is to present a new methodology for midterm energy forecasting in the framework of a data mining procedure. The method includes the development of a database that contains historical relevant data, such as values for energy consumption, weather parameters, statistical indices etc. The data is mined from the database, filtered, preprocessed and converted to desired forms. Data knowledge discovery is succeeded by constructing a non-linear multivariable regression model which takes in consideration correlation analysis on the selected variables. Results of the method for two types of customers, i.e. high voltage industries and residential customers are compared to standard regression methods. © 2003 IEEE.	en
heal.journalName	2003 IEEE Bologna PowerTech - Conference Proceedings	en
dc.identifier.doi	10.1109/PTC.2003.1304161	en
dc.identifier.volume	1	en
dc.identifier.spage	380	en
dc.identifier.epage	387	en