Innovative Hybrid Approach for Enhanced Renewable Energy Generation Forecasting Using Recurrent Neural Networks and Generative Adversarial Networks

Abstract

Renewable energy sources hold the key to a sustainable and green future, yet their inherent variability poses significant challenges for reliable power generation forecasting. In response to this critical issue, this study presents an innovative approach that harnesses the power of both Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) to revolutionize power generation forecasting in renewable energy systems. The hybrid model combines the strengths of RNNs, known for capturing temporal dynamics and sequential dependencies, and GANs, renowned for generating realistic data distributions. The results demonstrate a remarkable improvement in forecasting accuracy compared to traditional methods, reducing errors and uncertainties. The hybrid RNN-GAN model enhances the reliability of renewable energy systems, facilitating greater integration of sustainable energy sources into the grid. Furthermore, the research underscores the importance of incorporating a Grid-Connected Hybrid System Design and implementing a closed-loop control framework. These additions ensure that the forecasts are not just theoretical but are actively used to optimize energy utilization and maintain grid stability in real-world scenarios. This innovative approach holds great promise for a greener and more efficient energy landscape, making a substantial contribution to the transition towards a fresher and more sustainable future. The proposed Hybrid RNN-GAN model consistently outperforms existing methods, yielding significantly lower RMSE and MAE values for both solar and wind data, showcasing its superior accuracy in renewable energy generation forecasting. The achieved R-squared (R²) values of 0.82 for solar data and 0.7 for wind data at 100 iterations further validate the model's effectiveness in capturing underlying patterns, while skewness and kurtosis analyses affirm its ability to generate predictions aligned with normal distributions.