Comparison of Photovoltaic plant power production prediction methods using a large measured dataset G. Graditi * , S. Ferlito, G. Adinolfi ENEA Portici Research Center, Italy article info Article history: Received 24 August 2015 Received in revised form 14 November 2015 Accepted 5 January 2016 Available online 18 January 2016 Keywords: Artificial neural network Estimation Genetic algorithms MLP Photovoltaic power production Regression analysis abstract Nowadays the estimation of power production yield by stand-alone and grid-connected Photovoltaic (PV) plants is crucial for technical and economic feasibility design analyses. The main goal is to overcome renewables unpredictability by properly estimating the power production and by suitably balancing generation and consumption. In this context, many methods can be applied to forecast renewables energy production. The scope of this paper is a comparative analysis of three different methods to es- timate the power production of a preexisting PV plant. It is installed at ENEA Research Centre located in Portici (South Italy) and it is integrated in a Micro Grid (MG) configuration. In detail a phenomenological model proposed by Sandia National Laboratories and two statistical learning models, a Multi-Layer Perceptron (MLP) Neural Network and a Regression approach, are compared. These models are deeply different also in terms of required input data and parameters. In detail, phenomenological model application requires the availability of design parameters and technical devices specifications. Statistical machine learning models need, however, input variable previously acquired datasets. The a-Si/mc-Si PV plant, installed at Portici, represents an adequate case study for the three models comparison, as both design and acquired data are available. In fact, the plant was designed at the ENEA Research Centre so this makes possible the knowledge of the design parameters and, being a part of the MG, its data are continuously acquired and transmitted to other network devices. Obtained results demonstrate more accurate power predictions can be reached by statistical machine learning approaches. The main novelty of the paper consists in the optimization of the considered models by the appropriate identification of the minimum and more representative training dataset. Authors underline the unnecessary use of thousands samples by suitably selecting the dataset size and samples by means of a Genetic Algorithm. The optimization strategy effectiveness is verified comparing the prediction performances obtained employing the optimal dataset with those obtained with a randomly chosen dataset. In this scenario, Genetic Algorithm strategy represents a successful approach to the suitable identification of statistical models datasets. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction 1.1. Motivation and approach In the last years, global demand for electric energy is consider- ably increased, so requiring the identification of new energetic solutions. In this scenario, integration of renewable energies, in preexisting energy systems represents an important task to be considered [1]. At present, PV and wind technologies have reached high levels performances in terms of both efficiency and reliability, so representing promising solutions to the Zero Energy Balance application and realization. The price reduction and the provided user-friendly cable solutions have contributed to these limitless sources diffusion in civil and industrial contexts [2]. These systems are constituted by large amount of devices: PV generators [3], Distributed Maximum Power Point Tracking (DMPPT) converters [4,5], inverters, storage systems, grid interface devices, etc. Proper and efficient operating modes are assured only by conveniently sized and matched components. To achieve this aim, a previous accurate model development is an essential task. An aspect influencing renewable energy sources widespread is their intrinsic unpredictability. In fact, their power production * Corresponding author. E-mail address: giorgio.graditi@enea.it (G. Graditi). Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene http://dx.doi.org/10.1016/j.renene.2016.01.027 0960-1481/© 2016 Elsevier Ltd. All rights reserved. Renewable Energy 90 (2016) 513e519