Artificial neural network-based model for estimating the produced power of a photovoltaic module A. Mellit a, b, * , S. Sa  glam c , S.A. Kalogirou d a Faculty of Sciences and Technology, Renewable Energy Laboratory, Jijel University, Ouled-Aissa, P.O. Box .98, Jijel 18000, Algeria b Unité de développement des équipements solaires (UDES), Bousmail, Tipaza 42000, Algeria c Technical Education Faculty, Marmara University, Istanbul 34722, Turkey d Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, P.O. Box 50329, Limassol 3603, Cyprus article info Article history: Received 18 November 2012 Accepted 23 April 2013 Available online 18 May 2013 Keywords: Photovoltaic module Modelling Produced power ANN Forecasting abstract In this paper, a methodology to estimate the profile of the produced power of a 50 Wp Si-polycrystalline photovoltaic (PV) module is described. For this purpose, two artificial neural networks (ANNs) have been developed for use in cloudy and sunny days respectively. More than one year of measured data (solar irradiance, air temperature, PV module voltage and PV module current) have been recorded at the Marmara University, Istanbul, Turkey (from 1-1-2011 to 24-2-2012) and used for the training and vali- dation of the models. Results confirm the ability of the developed ANN-models for estimating the power produced with reasonable accuracy. A comparative study shows that the ANN-models perform better than polynomial regression, multiple linear regression, analytical and one-diode models. The advantage of the ANN-models is that they do not need more parameters or complicate calculations unlike implicit models. The developed models could be used to forecast the profile of the produced power. Although, the methodology has been applied for one polycrystalline PV module, it could also be generalized for large- scale photovoltaic plants as well as for other PV technologies. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction As reported by the IEA [1], global photovoltaic capacity has been increasing at an average annual growth rate of more than 40% since 2000 and it has significant potential for long-term growth over the next decades. By 2050, PV will provide 11% of global electricity production (4500 TWh per year), corresponding to 3000 GW of cumulative installed PV capacity. In countries like Turkey, photovoltaic research and development activities are still mainly undertaken across a range of universities, government and industry facilities and the projects are mainly financed by the research programme of State Planning Organiza- tion (DPT) and The Scientific & Research Council (TUBITAK) [2,3]. As the performance of photovoltaic systems is influenced by the magnitude of the insolation and atmospheric conditions, more ac- curate models of photovoltaic cell/module are required to estimate the produced power and generally to analyse the photovoltaic systems performance. As the modelling of photovoltaic cells/ modules is one of the most essential areas in photovoltaics research, numerous methods have been developed for modelling the IeV characteristic and estimating the maximal power. These can be generally classified into two types: explicit I ¼ f(V) and implicit I ¼ f(I, V) models. Explicit models employ a simple analytical expression based on assumptions and they need less computational effort. However, implicit models are relatively more accurate than the explicit ones, and they have the disadvantage of introducing a series of parameters which are difficult or even impossible to obtain from solar cell’s manufacturers (i.e., the series resistance, R S ; the shunt resistance, R Sh ; the dark saturation current, I 0 ; photo- generated current I ph and the diode ideality factor, n). Even if these parameters can be obtained empirically, designers of photo- voltaic systems often find difficulties in applying such models [4]. Artificial neural networks (ANNs), genetic algorithm (GA), neuro-fuzzy inference system (ANFIS) and particle swarm optimi- zation (PSO) techniques have been investigated in order to model and extract the PV cell/module parameters, as well as to estimate the maximum power. In Ref. [5] the authors used a neural network to estimate the maximum power generation from a PV module using environ- mental information. The proposed network can be utilized for the prediction of the next day’s generation from the PV systems by * Corresponding author. Abdus Salam International Centre for Theoretical Phys- ics, Strada Costiera, 11, 34151 Trieste, Italy. Tel.: þ213 (0)551 998 982. E-mail addresses: a.mellit@yahoo.co.uk, amellit@ictp.it (A. Mellit). Contents lists available at SciVerse ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene 0960-1481/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2013.04.011 Renewable Energy 60 (2013) 71e78