A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model

Precise power forecasting is important in contemporary energy management systems, especially for optimizing the use of renewable resources. However, precise predictions of solar and wind power face challenges due to their dependence on various environmental factors. In this research, we introduce a novel hybrid model that merges Convolutional Neural Network (CNN) and ResNet architectures with a Modified Recurrent Neural Network (MRNN). This MRNN model incorporates several key layers, including Bidirectional Long Short-Term Memory (BiLSTM), LSTM, and Bidirectional weighted Gated Recurrent Unit (BiGRU) layers. to address the constraints of conventional estimation methods and achieve precise short-term Photovoltaic and wind power prediction. Hyperparameters of the proposed CNN-MRNN model are optimized using the Grid Search CV algorithm, resulting in improved learning rate and performance. Comparative analysis with GCVCNN-RNN, GCVCNN-BiGRU,CNN-BiLSTM, and CNN-LSTM models demonstrates the superior performance of the proposed technique, achieving a 20% low mean absolute error (MAE) and 30% of root mean square error (RMSE). Additionally, the GCVCNN-MRNN model outperforms existing techniques based on Nash Sutcliffe analysis (0.9852) for time series forecasting. The experimental results validate the usefulness of the proposed GCVCNN-MRNN in correctly predicting PV and wind power, the model consistently achieves lower MAE and RMSE, along with higher correlation coefficient (CC) and R2, indicating improved accuracy and stability. This research contributes to the advancement of clean energy and expands the existing body of knowledge in power forecasting.