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Short-term load forecasting based on artificial neural networks parallel implementation.

Kalaitzakis Kostas, Stavrakakis Georgios, Anagnostakis E.

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URIhttp://purl.tuc.gr/dl/dias/48FF40D6-9765-4A7F-80B1-78F0206E3D79-
Identifierhttp://www.tuc.gr/fileadmin/users_data/elci/Kalaitzakis/J.22.pdf-
Identifierhttps://doi.org/10.1016/S0378-7796(02)00123-2-
Languageen-
Extent12en
TitleShort-term load forecasting based on artificial neural networks parallel implementation.en
CreatorKalaitzakis Kostasen
CreatorΚαλαϊτζακης Κωσταςel
CreatorStavrakakis Georgiosen
CreatorΣταυρακακης Γεωργιοςel
CreatorAnagnostakis E.en
PublisherElsevieren
DescriptionΔημοσίευση σε επιστημονικό περιοδικό el
Content SummaryThis paper presents the development and application of advanced neural networks to face successfully the problem of the short-term electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation, radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this paper. The performance of each presented structure is evaluated by means of an extensive simulation study, using actual hourly load data from the power system of the island of Crete, in Greece. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the load forecasting models proposed here provide significantly more accurate forecasts, compared to conventional autoregressive and BP forecasting models. Finally, a parallel processing approach for 24 h ahead forecasting is proposed and applied. According to this procedure, the requested load for each specific hour is forecasted, not only using the load time-series for this specific hour from the previous days, but also using the forecasted load data of the closer previous time steps for the same day. Thus, acceptable accuracy load predictions are obtained without the need of weather data that increase the system complexity, storage requirement and cost.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-09-30-
Date of Publication2002-
SubjectShort-term load forecastingen
SubjectMoving window regression trainingen
SubjectGaussian encoding neural networksen
SubjectRadial basis networksen
SubjectReal time recurrent neural networksen
Bibliographic CitationK. Kalaitzakis, G. Stavrakakis and E. Anagnostakis, "Short-term load forecasting based on artificial neural networks parallel implementation," Electric Power Systems Research, vol. 63, no. 3, pp. 185-196, Oct. 2002. doi:10.1016/S0378-7796(02)00123-2en

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