Improved solar energy forecasting using BiLSTM-driven GAN with transformed neural network

This paper presents a novel advancement in photovoltaic (PV) power prediction using modified architecture for a Generative Adversarial Network (GAN). The proposed GAN contains Bidirectional Long Short-Term Memory (BiLSTM) network as a generator, while the Transformed Neural Network (TransNN) acts as a discriminator. The generator uses historical PV data and environment factors to generate future power predictions, while the discriminator works on differentiating between true and synthetic data output, thus refining the generator. Such a combination of adversarial training will help the model learn complex temporal dependencies to provide good realistic PV forecasts. Experimental validation over benchmark datasets proves the model superior, achieving results such as Root Mean Square Error of 18.38, normalized RMSE (nRMSE) of 3.64, normalized Mean Absolute Error (nMAE) of 13.23, and Mean Absolute Bias Error (MABE) of 2.69. Unlike all this, the system also has a correlation coefficient (R) of 99.75%. This indicates how much the model aligns itself with PV outputs. This proposed GAN-based model provides a captivating degree of predictive accuracy and robustness over all other existing methods-the reason why this technique is viewed as an expected approach for developing solar energy forecasting into better integration and management of renewable energy with the grid.