A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks

Seyed Mahdi Miraftabzadeh (), Cristian Giovanni Colombo, Michela Longo () and Federica Foiadelli
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Seyed Mahdi Miraftabzadeh: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Cristian Giovanni Colombo: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Michela Longo: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Federica Foiadelli: Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy

Forecasting, 2023, vol. 5, issue 1, 1-16

Abstract: Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE.

Keywords: deep learning; transfer learning; photovoltaic production prediction; sequential model (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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