Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Application of Neural Networks Solar Radiation Prediction for Hybrid Renewable Energy Systems
verfasst von : P. Chatziagorakis, C. Elmasides, G. Ch. Sirakoulis, I. Karafyllidis, I. Andreadis, N. Georgoulas, D. Giaouris, A. I. Papadopoulos, C. Ziogou, D. Ipsakis, S. Papadopoulou, P. Seferlis, F. Stergiopoulos, S. Voutetakis
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.