Energy yield estimation of thin-film photovoltaic plants by using physical approach and artificial neural networks

Giorgio Graditi, Sergio Ferlito, Giovanna Adinolfi, Giuseppe Marco Tina, Cristina Ventura

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Nowadays, the estimation of the energy yield of a stand-alone or grid-connected photovoltaic (PV) systems is crucial for ensuring their economic feasibility and the proper sizing of system components. In fact, the energy yield estimation allows to avoid outages and it ensures quality and continuity of supply. In this context, this paper analyzes and compares two different approaches to estimate energy yield of a 1.05 kWpexperimental PV plant located at ENEA Portici Research Centre: the first one is based on the physical modelization of the plant; the other one is related to various topologies of Artificial Neural Networks (ANN). In particular, in the second case, a new hybrid method, called Hybrid Physical Artificial Neural Network (HPANN), based on an ANN and clear sky solar radiation curves is proposed and compared with a Multi-Layer Perceptron (MLP) ANN method widely used in the scientific literature. Moreover, using the same structure of the HPANN, a nonlinear AutoRegressive eXogenous (ARX) model, which uses a wavelet network as its nonlinearity estimator, and an approach founded on Adaptive Network based Fuzzy Inference System (FIS) have also been developed.In order to verify the effectiveness of the implemented approaches, measured and estimated data have been compared and errors have been calculated by means of different statistical coefficients. Results demonstrate that the HPANN approach allows a more precise estimation of the ac energy yield, obtaining, in the worst case, values of Relative Root Mean Square Error less than 10%.
Original languageEnglish
Pages (from-to)232 - 243
Number of pages12
JournalSolar Energy
Volume130
DOIs
Publication statusPublished - 1 Jun 2016

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All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Materials Science(all)

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