Prediction of photovoltaic energy generation using recurrent and transformer neural networks

Precise prediction of photovoltaic (PV) energy generation is essential for op-timal, profitable and ecological management of electric energy resources all over the world. As a result, attempts are being made to develop more accurate prediction algo-rithms. This paper compares the application of Long Short-Term Memory (LSTM, a sub-type of Recurrent Neural Networks), PatchTST (a type of Transformer Neural Network - TNN) and ensemble models (making use of these two approaches) for estimating PV en-ergy 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 Austral-ian La Trobe University. However, future studies could verify this approach using differ-ent 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.