Abstract:
Since solar and wind energy are becoming more and more integrated into electrical grids, accurate forecasting of renewable energy
generation is essential for modern power systems. However, accurate prediction is difficult due to the highly sporadic and nonlinear
nature of these energy sources, which are driven by atmospheric and environmental variability. The majority of current research
concentrates on predicting solar or wind energy separately, frequently employing various preprocessing techniques, models, and
assessment approaches, which restricts fair comparison and useful scalability. In order to predict solar and wind energy generation
within a single methodological pipeline, this study suggests a unified deep learning-based forecasting framework that uses a hybrid
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model. The suggested framework ensures consistency and cross
dataset comparability by applying the same data preprocessing procedures, sequence generation strategies, model architecture, and
evaluation metrics to both energy sources. To efficiently capture historical dependencies, time-series data are normalized using Min
Max scaling and converted into fixed-length temporal sequences. The hybrid LSTM–GRU architecture is made to take advantage of the
complementary capabilities of both recurrent networks, with GRU layers effectively capturing shortterm fluctuations and LSTM layers
modeling long-term temporal patterns. To evaluate the effect of dynamic temporal weighting on forecasting performance, adaptive
attentionbased variations are also investigated. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), accuracy obtained from
confusion matrix analysis, and visual inspection using actual-versus-predicted plots are used to assess model performance. According
to experimental results, the Hybrid LSTM–GRU model without adaptive attention outperforms standalone LSTM, GRU, and attention
enhanced variants, achieving the highest forecasting accuracy of 92.61% for solar energy and 93.90% for wind energy. These results
validate the ability of a single hybrid deep learning framework to efficiently generalize across various renewable energy sources. The
suggested method supports better energy planning, grid stability, and decision-making in smart power systems by providing a scalable
and workable solution for integrated renewable energy forecasting.
