Midterm energy forecasting using fuzzy logic: A comparison of confidence interval estimation techniques

Elias, ChN; Tsekouras, GJ

dc.contributor.author	Elias, ChN	en
dc.contributor.author	Tsekouras, GJ	en
dc.date.accessioned	2014-03-01T02:53:21Z
dc.date.available	2014-03-01T02:53:21Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36261
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-82955190415&partnerID=40&md5=e8ce457b2241ce9ccac3898b0eeebf94	en
dc.subject	Confidence interval	en
dc.subject	Fuzzy logic	en
dc.subject	Midterm energy forecasting	en
dc.subject	Re-sampling technique	en
dc.subject.other	Confidence interval	en
dc.subject.other	Confidence interval estimation	en
dc.subject.other	Energy demands	en
dc.subject.other	Energy forecasting	en
dc.subject.other	Energy prediction	en
dc.subject.other	Forecasting methods	en
dc.subject.other	Fuzzy logic method	en
dc.subject.other	Mathematical method	en
dc.subject.other	Power system loads	en
dc.subject.other	Resampling	en
dc.subject.other	Standard deviation	en
dc.subject.other	Training sets	en
dc.subject.other	Electric load forecasting	en
dc.subject.other	Energy management	en
dc.subject.other	Estimation	en
dc.subject.other	Financial data processing	en
dc.subject.other	Forecasting	en
dc.subject.other	Fuzzy neural networks	en
dc.subject.other	Membership functions	en
dc.subject.other	Power transmission	en
dc.subject.other	Sampling	en
dc.subject.other	Systems science	en
dc.subject.other	Fuzzy logic	en
dc.title	Midterm energy forecasting using fuzzy logic: A comparison of confidence interval estimation techniques	en
heal.type	conferenceItem	en
heal.publicationDate	2011	en
heal.abstract	The modern methods for power system load and energy prediction are usually based on artificial neural networks and fuzzy logic, which present satisfactory results. However, the estimation of the confidence intervals can not be applied directly, unlike to the classical forecasting methods. The objective of this paper is to present an optimized fuzzy logic method for midterm energy forecasting, which can use different techniques for the estimation of the confidence interval, such as the statistical calculation based on the forecasting method errors of the training set, the re-sampling technique and a novel analytical mathematical method based on the membership functions. Finally, the next annual energy demand of Greek interconnected power system is estimated analytically. Simultaneously, the standard deviations through the aforementioned techniques are calculated and compared.	en
heal.journalName	Recent Researches in System Science - Proceedings of the 15th WSEAS International Conference on Systems, Part of the 15th WSEAS CSCC Multiconference	en
dc.identifier.spage	446	en
dc.identifier.epage	452	en