Short term load forecasting in Greek power system using ANNs

dc.contributor.author	Τσεκούρας, Γιώργος	el
dc.contributor.author	Μαστοράκης, Ν.	el
dc.contributor.author	Κανέλλος, Φ.	el
dc.contributor.author	Κονταργύρη, Βασιλική	el
dc.contributor.author	Τσιρέκης, Κωνσταντίνος	el
dc.date.accessioned	2015-05-25T18:17:09Z
dc.date.available	2015-05-25T18:17:09Z
dc.date.issued	2015-05-25
dc.identifier.uri	http://hdl.handle.net/11400/11141
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.wseas.org	en
dc.subject	Artificial neural networks
dc.subject	Resampling technique
dc.subject	Short-term load forecasting
dc.subject	Τεχνητά δίκτυα νεύρων
dc.subject	Τεχνική αναδειγματοληψίας
dc.subject	Βραχυπρόθεσμη πρόβλεψη φορτίου
dc.title	Short term load forecasting in Greek power system using ANNs	en
heal.type	conferenceItem
heal.secondaryTitle	confidence interval estimation using a novel re-sampling technique with corrective factor	en
heal.classification	Technology
heal.classification	Energy
heal.classification	Τεχνολογία
heal.classification	Ενέργεια
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://id.loc.gov/authorities/names/n42028321
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ενέργεια
heal.contributorName	Καρανάσιου, Ι.	el
heal.contributorName	Ηλίας, Χρήστος	el
heal.contributorName	Σαλής, Α.	el
heal.contributorName	Κονταξής, Παναγιώτης	el
heal.contributorName	Γιαλκέτση, Α.	el
heal.language	en
heal.access	campus
heal.publicationDate	2010-12-29
heal.bibliographicCitation	Tsekouras, G., Mastorakis, N., Kanellos, F., Kontargyri, V., Tsirekis, C. et al. (2010) Short term load forecasting in Greek power system using ANNs: Confidence interval estimation using a novel re-sampling technique with corrective factor. In 9th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing. 29th to 31st December 2010. Athens	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.fullTextAvailability	true
heal.conferenceName	9th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing	en
heal.conferenceItemType	poster