Photovoltaic (PV) energy systems are receiving increasing attention, given their relative ease of installation, with 3rd generation technologies promising even simpler fabrication processes and less-intrusive installation possibilities. Therefore, methods for predicting the PV energy output are important to balance the production of other types of renewable sources and avoid wasting energy, with approaches based on machine learning models being especially studied in recent applications. In the case of new-generation cells, limited data is available to train such models, making the use of transfer learning a viable approach to increase prediction accuracy. However, no work in the literature has considered a transfer learning approach studying how much knowledge can be transferred between 2nd and 3rd generation PV technologies. In this paper, we propose the first approach in the literature based on machine learning and transfer learning for the PV energy prediction, in the case of new-generation PV technologies for which limited training data is available. We tested our method on data collected from several locations throughout the world, with results confirming the validity of the approach.

Photovoltaic energy prediction for new-generation cells with limited data: A transfer learning approach / A. Genovese, V. Bernardoni, V. Piuri, F. Scotti, F. Tessore (... IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE.). - In: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)[s.l] : IEEE, 2022 May 16. - ISBN 978-1-6654-8360-5. - pp. 1-6 (( Intervento presentato al 12. convegno International Instrumentation and Measurement Technology Conference tenutosi a Ottawa nel 2022 [10.1109/I2MTC48687.2022.9806492].

Photovoltaic energy prediction for new-generation cells with limited data: A transfer learning approach

A. Genovese
Primo
;
V. Bernardoni
Secondo
;
V. Piuri;F. Scotti
Penultimo
;
F. Tessore
Ultimo
2022

Abstract

Photovoltaic (PV) energy systems are receiving increasing attention, given their relative ease of installation, with 3rd generation technologies promising even simpler fabrication processes and less-intrusive installation possibilities. Therefore, methods for predicting the PV energy output are important to balance the production of other types of renewable sources and avoid wasting energy, with approaches based on machine learning models being especially studied in recent applications. In the case of new-generation cells, limited data is available to train such models, making the use of transfer learning a viable approach to increase prediction accuracy. However, no work in the literature has considered a transfer learning approach studying how much knowledge can be transferred between 2nd and 3rd generation PV technologies. In this paper, we propose the first approach in the literature based on machine learning and transfer learning for the PV energy prediction, in the case of new-generation PV technologies for which limited training data is available. We tested our method on data collected from several locations throughout the world, with results confirming the validity of the approach.
Photovoltaic (PV); Dye-Sensitized Solar Cells (DSSCs); Feedforward Neural Networks (FFNN); Transfer Learning; Renewable Energy
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
16-mag-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/912286
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