Accurate Hourly Forecasting of Wind Energy in Romania Using Deep Learning Models

Wind energy plays a critical role in the European Union’s decarbonization strategy, including Romania’s growing renewable energy capacity. This study proposes a deep learning-based method for forecasting hourly wind energy production in Romania using feedforward neural networks (FFNNs) and recurrent neural networks (RNNs), trained on a dataset spanning from 1 January to 31 December 2023. The dataset includes hourly wind energy output data (mean = 850.6 MW, std = 694.0 MW) and 13 meteorological variables (e.g., average wind speed = 4.7 km/h, temperature = 14.4 °C). A total of 1296 models were trained and evaluated, with the best-performing RNN model achieving a coefficient of determination of R2 = 0.9680 and a mean absolute error (MAE) of 81.03 MW. The top three models all exceeded R2 = 0.966, demonstrating strong generalization on unseen data. The models were also validated using two external time intervals outside the training/testing sets, confirming robustness. These results show that deep learning models can provide highly accurate, data-driven predictions of wind energy output, supporting grid stability and informed decision-making amid renewable energy variability.