Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems. In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power pro-duced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes.The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered. The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.

Hourly forecasting of the photovoltaic electricity at any latitude using a network of artificial neural networks

Baglivo, C
Penultimo
;
Congedo, PM
Ultimo
2023-01-01

Abstract

Nowadays, special attention is paid to the importance of using photovoltaic (PV) systems to tackle the problem of climate change and the energy crisis. Artificial intelligence is currently used in different science fields for its great potential and accuracy in forecasting problems. In this work, a network of artificial neural networks (ANNs) was trained and validated to forecast the hourly worldwide electrical power produced by various PV modules, with different electrical characteristics. Each ANN describes the worldwide performance of each PV module on the optimal inclination angle. The training data consists of the hourly air temperature, horizontal total solar radiation as input data and electrical power pro-duced as output. The power is obtained from the hourly simulation of PV modules with an electrical circuit model in 24 localities at very different latitudes.The validation and generalization of the network were obtained by considering the six PV modules in further 24 localities and by considering two further PV modules in all 48 localities considered. The excellent results in terms of accuracy metrics confirmed that the network of ANNs is a reliable, simple and accurate tool that can be used to predict the hourly performance of any PV module in any location worldwide.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S221313882300190X-main.pdf

solo utenti autorizzati

Descrizione: Articolo
Tipologia: Versione editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 1.64 MB
Formato Adobe PDF
1.64 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/490768
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 11
social impact