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

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dc.contributor.author Pappas, S en
dc.contributor.author Ekonomou, L en
dc.contributor.author Karampelas, P en
dc.contributor.author Karamousantas, D en
dc.contributor.author Katsikas, S en
dc.contributor.author Chatzarakis, G en
dc.contributor.author Skafidas, P en
dc.date.accessioned 2014-03-01T01:59:17Z
dc.date.available 2014-03-01T01:59:17Z
dc.date.issued 2010 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/28888
dc.subject akaike information criterion en
dc.subject arma model en
dc.subject bayesian information criterion en
dc.subject Electricity Consumption en
dc.subject Electricity Prices en
dc.subject Information Criterion en
dc.subject Load Forecasting en
dc.subject Model Specification en
dc.subject Modeling and Forecasting en
dc.subject Order Selection en
dc.subject Parameter Estimation en
dc.subject Power System en
dc.subject Time Series Analysis en
dc.subject kalman filter en
dc.title Electricity demand load forecasting of the Hellenic power system using an ARMA model en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.epsr.2009.09.006 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.epsr.2009.09.006 en
heal.publicationDate 2010 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 en
heal.journalName Electric Power Systems Research en
dc.identifier.doi 10.1016/j.epsr.2009.09.006 en


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