Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

Martina Radicioni, Valentina Lucaferri, Francesco De Lia, Antonino Laudani, Roberto Lo Presti, Gabriele Maria Lozito, Francesco Riganti Fulginei, Riccardo Schioppo and Mario Tucci
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Martina Radicioni: Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
Valentina Lucaferri: Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
Francesco De Lia: Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy
Antonino Laudani: Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
Roberto Lo Presti: Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy
Gabriele Maria Lozito: Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
Francesco Riganti Fulginei: Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
Riccardo Schioppo: Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy
Mario Tucci: Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy

Energies, 2021, vol. 14, issue 3, 1-22

Abstract: This work proposes an Artificial Neural Network (ANN) able to provide an accurate forecasting of power produced by photovoltaic (PV) plants. The ANN is customized on the basis of the particular season of the year. An accurate analysis of input variables, i.e., solar irradiance, temperature and air humidity, carried out by means of Pearson Correlation, has allowed to select, day by day, the most suitable set of inputs and ANN architecture also to reduce the necessity of large computational resource. Thus, features are added to the ANN as needed, avoiding waste of computational resources. The method has been validated through data collected from a PV plant installed in ENEA (National agency for new technologies, energy and sustainable economic development) Research Center, located in Casaccia, Rome (Italy). The developed strategy is able to furnish accurate predictions even in the case of strong irregularities of solar irradiance, providing accurate results in rapidly changing scenarios.

Keywords: photovoltaic; artificial neural network; PV power; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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