Prediction of photovoltaic energy generation using recurrent and transformer neural networks

Precise prediction of photovoltaic (PV) energy generation is essential for optimal, profitable and ecological management of electric energy resources all over the world. As a result, attempts are being made to develop more accurate prediction algorithms. This paper compares the application of Long Short-Term Memory (LSTM, a subtype of Recurrent Neural Networks), PatchTST (a type of Transformer Neural Network – TNN) and ensemble models (making use of these two approaches) for estimating PV energy production 24 hours ahead. The results indicate that both analysed single methods have comparable prediction accuracy, though the hybrid approach outperforms them. The experiments were conducted on data from PV sites deployed across campuses at Australian La Trobe University. However, future studies could verify this approach using different datasets. Algorithms and results presented in this study may especially contribute to the development of Recurrent and Transformer Neural Networks as prediction methods of PV energy production.

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).