Short term load forecasting in interconnected greek power system using ANN: Confidence interval estimation using a novel re-sampling technique with corrective factor

Tsekouras, GJ; Mastorakis, NE; Kanellos, FD; Kontargyri, VT; Tsirekis, CD; Karanasiou, IS; Elias, ChN; Salis, AD; Contaxis, PA; Gialketsi, AA

dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Mastorakis, NE	en
dc.contributor.author	Kanellos, FD	en
dc.contributor.author	Kontargyri, VT	en
dc.contributor.author	Tsirekis, CD	en
dc.contributor.author	Karanasiou, IS	en
dc.contributor.author	Elias, ChN	en
dc.contributor.author	Salis, AD	en
dc.contributor.author	Contaxis, PA	en
dc.contributor.author	Gialketsi, AA	en
dc.date.accessioned	2014-03-01T02:52:45Z
dc.date.available	2014-03-01T02:52:45Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36043
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-79959890401&partnerID=40&md5=9c4cf5165574b415cef8108e4b5927f8	en
dc.subject	Artificial neural networks	en
dc.subject	Confidence interval	en
dc.subject	Re-sampling technique	en
dc.subject	Short-term load forecasting	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Confidence interval	en
dc.subject.other	Confidence interval estimation	en
dc.subject.other	Corrective factor	en
dc.subject.other	Forecasting methods	en
dc.subject.other	Load demand	en
dc.subject.other	Multiplication factor	en
dc.subject.other	Power system loads	en
dc.subject.other	Resampling	en
dc.subject.other	Resampling method	en
dc.subject.other	Short term load forecasting	en
dc.subject.other	Short-term forecasting	en
dc.subject.other	Test sets	en
dc.subject.other	Training data sets	en
dc.subject.other	Training sets	en
dc.subject.other	Financial data processing	en
dc.subject.other	Forecasting	en
dc.subject.other	Networks (circuits)	en
dc.subject.other	Neural networks	en
dc.subject.other	Power transmission	en
dc.subject.other	Signal processing	en
dc.subject.other	Electric load forecasting	en
dc.title	Short term load forecasting in interconnected greek power system using ANN: Confidence interval estimation using a novel re-sampling technique with corrective factor	en
heal.type	conferenceItem	en
heal.publicationDate	2010	en
heal.abstract	The modern methods for power system load prediction are usually based on Artificial Neural Networks (ANN), which present satisfactory results. However, the estimation of the confidence intervals can not be applied directly, unlike to the classical forecasting methods. One of the most commonly used methods is the re-sampling technique, which calculates the respective confidence interval based on the training data set. The limits of the training set confidence interval are also applied in the case of the real prediction giving satisfactory but slightly underestimated results. The targets of this paper are: (1) to apply the basic re-sampling method for the short term forecasting of the next day load in the interconnected Greek power system using an optimized ANN proving the aforementioned disadvantage and (2) to propose a modified re-sampling technique using a proper corrective multiplication factor. Finally, the next day load demand of the test set is estimated using the best ANN structure and the modified confidence intervals.	en
heal.journalName	International conference on Circuits, Systems, Electronics, Control and Signal Processing - Proceedings	en
dc.identifier.spage	166	en
dc.identifier.epage	172	en