Short term load forecasting in greek intercontinental power system using ANNs

dc.contributor.author	Τσεκούρας, Γιώργος	el
dc.contributor.author	Κανέλλος, Φ.	el
dc.contributor.author	Κονταργύρη, Βασιλική	el
dc.contributor.author	Τσιρέκης, Κωνσταντίνος	el
dc.contributor.author	Καρανάσιου, Χριστίνα	el
dc.date.accessioned	2015-05-25T17:55:42Z
dc.date.available	2015-05-25T17:55:42Z
dc.date.issued	2015-05-25
dc.identifier.uri	http://hdl.handle.net/11400/11139
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	Input variables
dc.subject	Short-term load forecasting
dc.subject	Τεχνητά δίκτυα νεύρων
dc.subject	Μεταβλητές εισόδου
dc.subject	Βραχυπρόθεσμη πρόβλεψη φορτίου
dc.title	Short term load forecasting in greek intercontinental power system using ANNs	en
heal.type	conferenceItem
heal.secondaryTitle	a study for input variables	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	Ηλίας, Χρήστος	el
heal.contributorName	Σαλής, Α.	el
heal.contributorName	Κονταξής, Παναγιώτης	el
heal.contributorName	Μαστοράκης, Ν.	el
heal.language	en
heal.access	campus
heal.publicationDate	2009-03-23
heal.bibliographicCitation	Tsekouras, G., Kanellos, F., Kontargyri, V., Tsirekis, C., Karanasiou, I. et al. (2008) Short term load forecasting in Greek intercontinental power system using ANNs: a study for input variables. In 10th WSEAS International Conference on Neural Networks. 23rd to 25th March 2009. Prague	en
heal.abstract	The scopus of this paper is to compare the performance of different structures of Artificial Neural Networks (ANNs) regarding the input variables used for short-term forecasting of the next day load in intercontinental Greek power system. The input variables can be: (a) historical loads, (b) weather related temperatures, (c) hour and day indicators, in two ways: (i) selfsame, (ii) compressed using the Principal Components Analysis (PCA). The training algorithm is the scaled conjugate gradient algorithm, for which a calibration process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, etc. The performance of each structure is evaluated by the Mean Absolute Percentage Error (MAPE) between the experimental and estimated values of the hourly load demand of the next day for the evaluation set in order to specify the optimal ANN. Finally the load demand for the next day of the test set (with the historical data of the current year) is estimated using the best ANN structure, so that the verification of behaviour of ANN load prediction techniques was demonstrated.	en
heal.fullTextAvailability	true
heal.conferenceName	10th WSEAS International Conference on Neural Networks	en
heal.conferenceItemType	poster