Application of Neural Networks Solar Radiation Prediction for Hybrid Renewable Energy Systems

Voutetakis, Spyros/ Stergiopoulos, Fotis/ Papadopoulos, Athanasios/ Papadopoulou, Simira/ Seferlis, Panos/ Ziogou, Chrysovalantou/ Ipsakis, Dimitris/ Giaouris, Damian/ Georgoulas, Nikolaos/ Chatziagorakis, Prodromos/ Karafyllidis, Ioannis/ Andreadis, Ioannis/ Elmasides, Costas/ Sirakoulis, Georgios/ Σεφερλής, Πάνος/ Στεργιόπουλος, Φώτης/ Ιψάκης, Δημήτρης/ Παπαδοπούλου, Σημίρα/ Βουτετάκης, Σπύρος/ Ζιώγου, Xρυσοβαλάντου/ Παπαδόπουλος, Αθανάσιος/ Γκιαούρης, Δαμιανός/ Γεωργουλάς, Νικόλαος/ Ανδρεάδης, Ιωάννης/ Καραφυλλίδης, Ιωάννης/ Συρακούλης, Γεώργιος/ Ελμασίδης, Κωνσταντίνος/ Χατζηαγοράκης, Πρόδρομος

Institution and School/Department of submitter: ΤΕΙ Θεσσαλονίκης
Keywords: Hybrid Renewable Energy System;Power Management Strategy;Solar Radiation;Recurrent Neural Network
Issue Date: 5-Sep-2014
Publisher: Springer International Publishing
Citation: Sofia.15th International Conference. (2014) Application of Neural Networks Solar Radiation Prediction for Hybrid Renewable Energy Systems. Switzerland: Springer International Publishing (133-144)
InterInternational Conferencenational Conference, Sofia, 2014
Abstract: In this paper a Recurrent Neural Network (RNN) for solar radiation prediction is proposed for the enhancement of the Power Management Strategies (PMSs) of Hybrid Renewable Energy Systems (HYRES). The presented RNN can offer both daily and hourly prediction concerning solar irradiation forecasting. As a result, the proposed model can be used to predict the Photovoltaic Systems output of the HYRES and provide valuable feedback for PMSs of the understudy autonomous system. To do so a flexible network based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of SYSTEMS SUNLIGHT S.A. facilities. As a result, the RNN after training with meteorological data of the aforementioned area is applied to the specific HYRES and successfully manages to enhance and optimize its PMS based on the provided solar radiation prediction.
Description: Δημοσιεύσεις μελών--ΣΤΕΦ--Τμήμα Αυτοματισμού, 2015
ISBN: 978-3-319-11070-7
Other Identifiers: 10.1007/978-3-319-11071-4_13
Appears in Collections:Δημοσιεύσεις σε Περιοδικά

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