In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems’ utilization and overall process optimization. For this purpose, 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. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.
Recurrent neural network Solar radiation Power management strategy Hybrid renewable energy system
This is a preview of subscription content, log in to check access.
This work is co-financed by National Strategic Reference Framework (NSRF) 2007–2013 of Greece and the European Union Research Program “SYNERGASIA” (SUPERMICRO – 09ΣYN-32-594).
Zakeri B, Syri S (2015) Electrical energy storage systems: a comparative life cycle cost analysis. Renew Sustain Energy Rev 42:569–596CrossRefGoogle Scholar
Garcıa P, Torreglosa JP, Fernandez LM, Jurado F (2013) Optimal energy management system for standalone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic. Int J Hydrogen Energy 38:14146–14158CrossRefGoogle Scholar
Zhang X, Chan SH, Ho HK, Tan S-C, Li M, Li G, Li J, Feng Z (2015) Towards a smart energy network: the roles of fuel/electrolysis cells and technological perspectives. Int J Hydrogen Energy 40:6866–6919CrossRefGoogle Scholar
Deshmukha MK, Deshmukh SS (2008) Modeling of hybrid renewable energy systems. Renew Sustain Energy Rev 12(1):235–249CrossRefGoogle Scholar
Alam S, Kaushik SC, Garg SN (2006) Computation of beam solar radiation at normal incidence using artificial neural network. Renew Energy 31(10):1483–1491CrossRefGoogle Scholar
Mubiru J, Banda EJKB (2008) Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol Energy 82(2):181–187CrossRefGoogle Scholar
Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2):571–576CrossRefGoogle Scholar
Ghanbarzadeh A, Noghrehabadi R, Assareh E, Behrang MA (2009) Solar radiation forecasting using meteorological data. In: 7th IEEE international conference on industrial informatics (INDIN 2009), UKGoogle Scholar
Benghanem M, Mellit A (2010) Radial basis function network-based prediction of global solar radiation data: application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia. Energy 35:3751–3762CrossRefGoogle Scholar
Paoli C, Voyant C, Muselli M, Nivet ML (2010) Forecasting of preprocessed daily solar radiation time series using neural networks. Sol Energy 84(12):2146–2160CrossRefGoogle Scholar
AbdulAzeez MA (2011) Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau, Nigeria. Arch Appl Sci Res 3(2):586–595Google Scholar
Mellit A, Kalogirou SA, Hontoria L, Shaari S (2009) Artificial intelligence techniques for sizing photovoltaic systems: a review. Renew Sustain Energy Rev 13(2):406–419CrossRefGoogle Scholar
Zeng Z, Yang H, Zhao R, Meng J (2013) Nonlinear characteristics of observed solar radiation data. Sol Energy 87:204–218CrossRefGoogle Scholar
Zhang N, Behera, PK (2012) Solar radiation prediction based on recurrent neural networks trained by Levenberg–Marquardt backpropagation learning algorithm. In: Innovative smart grid technologies (ISGT), 2012 IEEE PES, pp 1–7Google Scholar
Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall, Englewood CliffsMATHGoogle Scholar
Anderson JA, Rosenfield E (1989) Neurocomputing: foundations of research. MIT Press, CambridgeGoogle Scholar
Giaouris D, Papadopoulos AI, Ziogou C, Ipsakis D, Voutetakis S, Papadopoulou S, Seferlis P, Stergiopoulos F, Elmasides C (2013) Performance investigation of a hybrid renewable power generation and storage system using systemic power management models. Energy 61:621–635CrossRefGoogle Scholar
Chatziagorakis P, Elmasides C, Sirakoulis GCh et al (2014) Application of neural networks solar radiation prediction for hybrid renewable energy systems. In: Mladenov V et al (eds) EANN 2014, CCIS, vol 459. Sofia, Bulgaria, pp 133–144Google Scholar
Ipsakis D, Voutetakis S, Seferlis P, Stergiopoulos F, Elmasides C (2009) Power management strategies on a stand-alone power system using renewable energy sources and hydrogen storage. Int J Hydrogen Energy 34:7081–7095CrossRefGoogle Scholar