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 |