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

P. Chatziagorakis, C.Elmasides, G.C. Sirakoulis, I. Karafyllidis, I. Andreadis, N. Georgoulas, D. Giaouris, A.I. Papadopoulos, C. Ziogou, D. Ipsakis, S. Papadopoulou, P. Seferlis, F. Stergiopoulos, S. Voutetakis
2014. Communications in Computer and Information Science, 459, 133-144, doi: 10.1007/978-3-319-11071-4_13

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.

Keywords: Recurrent Neural Network, Solar Radiation, Power Management Strategy, Hybrid Renewable Energy System