Electricity demand load forecasting of the Hellenic power system using an ARMA model

dc.contributor.author	Παππάς, Στυλιανός Σ.	el
dc.contributor.author	Οικονόμου, Λάμπρος	el
dc.contributor.author	Καράμπελας, Παναγιώτης	el
dc.contributor.author	Καραμουσάντας, Δημήτριος Χ.	el
dc.contributor.author	Κατσικάς, Σωκράτης Κ.	el
dc.date.accessioned	2015-06-06T14:02:54Z
dc.date.available	2015-06-06T14:02:54Z
dc.date.issued	2015-06-06
dc.identifier.uri	http://hdl.handle.net/11400/15315
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.sciencedirect.com/science/article/pii/S0378779609002107	en
dc.subject	Parameter estimation
dc.subject	Forecasting
dc.subject	Προσδιορισμός παραμέτρων
dc.subject	Πρόβλεψη
dc.title	Electricity demand load forecasting of the Hellenic power system using an ARMA model	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Energy
heal.classification	Τεχνολογία
heal.classification	Ενέργεια
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://zbw.eu/stw/descriptor/14175-2
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ενέργεια
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85097853
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh00005779
heal.contributorName	Χατζαράκης, Γεώργιος Ε.	el
heal.contributorName	Σκαφίδας, Παναγιώτης Δ.	el
heal.identifier.secondary	doi:10.1016/j.epsr.2009.09.006
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010-03
heal.bibliographicCitation	Pppas, S., Ekonomou, L., Karampelas, P., Karamousantas, D., Katsikas, S., et al. (March 2010). Electricity demand load forecasting of the Hellenic power system using an ARMA model. Electric Power Systems Research. 80(3). pp. 256-264. Elsevier B.V: 2010. Available from: http://www.sciencedirect.com/science/article/pii/S0378779609002107 [Accessed 17/10/2009]	en
heal.abstract	Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Electric Power Systems Research	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true