Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών

Μόνιμο URI για αυτήν την κοινότηταhttps://dspace.library.tuc.gr/handle/123456789/2

Μέχρι και τον Ιούνιο του 2016, η Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών ονομαζόταν Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστών.

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Πλοήγηση Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών ανά Συγγραφέα "Anagnostakis E."

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    Short-term load forecasting based on artificial neural networks parallel implementation.
    (Elsevier, 2002) Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Stavrakakis Georgios; Σταυρακακης Γεωργιος; Anagnostakis E.
    This 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.