An optimized adaptive neural network for annual midterm energy forecasting

Tsekouras, GJ; Hatziargyriou, ND; Dialynas, EN

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Dialynas, EN	en
dc.date.accessioned	2014-03-01T01:23:35Z
dc.date.available	2014-03-01T01:23:35Z
dc.date.issued	2006	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17034
dc.subject	Adaptive artificial neural network (ANN)	en
dc.subject	Energy forecasting	en
dc.subject	Optimization of ANN parameters	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Data processing	en
dc.subject.other	Electric load management	en
dc.subject.other	Flowcharting	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Regression analysis	en
dc.subject.other	Sensitivity analysis	en
dc.subject.other	Adaptive artificial neural network	en
dc.subject.other	Energy forecasting	en
dc.subject.other	Input variables	en
dc.subject.other	Standard regression methods	en
dc.subject.other	Electric industry	en
dc.title	An optimized adaptive neural network for annual midterm energy forecasting	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2005.860926	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2005.860926	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	The objective of this paper is to present a new methodology for midterm energy forecasting. The proposed model is an adaptive artificial neural network (ANN), which properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set. The ANN parameters, such as the finally used input variables, the number of neurons, initial values, and time periods of momentum term and training rate, are simultaneously selected by an optimization process. Another characteristic of the model is the use of a minimal training set of patterns. Results from an extensive analysis conducted by the developed method for the Greek power system and for different categories of customers are compared to those obtained from the application of standard regression methods. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2005.860926	en
dc.identifier.isi	ISI:000235017000046	en
dc.identifier.volume	21	en
dc.identifier.issue	1	en
dc.identifier.spage	385	en
dc.identifier.epage	391	en