An annual midterm energy forecasting model using fuzzy logic

Elias, CN; Hatziargyriou, ND

dc.contributor.author	Elias, CN	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:29:49Z
dc.date.available	2014-03-01T01:29:49Z
dc.date.issued	2009	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19358
dc.subject	Energy forecasting	en
dc.subject	Fuzzy logic	en
dc.subject	Optimization of membership functions	en
dc.subject	Standard deviation	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Backpropagation	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Forecasting	en
dc.subject.other	Fourier transforms	en
dc.subject.other	Functions	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Probability density function	en
dc.subject.other	Standards	en
dc.subject.other	Statistics	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Energy forecasting	en
dc.subject.other	Energy values	en
dc.subject.other	Fuzzy logic methods	en
dc.subject.other	Input variables	en
dc.subject.other	Mathematical expressions	en
dc.subject.other	Optimization of membership functions	en
dc.subject.other	Optimization process	en
dc.subject.other	Power systems	en
dc.subject.other	Regression methods	en
dc.subject.other	Standard deviation	en
dc.subject.other	Training sets	en
dc.subject.other	Triangular membership functions	en
dc.subject.other	Membership functions	en
dc.title	An annual midterm energy forecasting model using fuzzy logic	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2008.2009490	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2008.2009490	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	The objective of this paper is to present a new fuzzy logic method for midterm energy forecasting. The proposed method properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set and to use a minimal number of patterns. The input variables, the number of the triangular membership functions and their base widths are simultaneously selected by an optimization process. The standard deviation is calculated analytically by mathematical expressions based on the membership functions. Results from an extensive application of the method to the Greek power system and for different categories of customers are compared to those obtained from the application of standard regression methods and artificial neural networks (ANN). © 2009 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.2008.2009490	en
dc.identifier.isi	ISI:000262817200050	en
dc.identifier.volume	24	en
dc.identifier.issue	1	en
dc.identifier.spage	469	en
dc.identifier.epage	478	en