Deep Learning Driven Short Term Solar Radiation Forecasting System Using Temporal Attention Gated Convolutional Network Model

Forecasting solar irradiance is very important for improving the effectiveness and dependability of solar energy systems. Predicting short-term changes in solar irradiance is crucial for different uses, such as managing energy, integrating with power grids, and planning operations. In this area, the suggested framework gives a new method by merging complex machine learning models with Temporal Attention Gated Convolutional Network or TAGC-Net design and Generative Adversarial Network (GAN). The proposed work integrates TAGC-Net to catch time dynamics with GAN's expertise in modeling difficult data distributions, giving a joint improvement that boosts forecasting accuracy and trustworthiness. In this regard, TAGC-Net is generally applied to model temporal dynamics and patterns of sequential solar irradiance data to ensure enhancement in various ways, including improved short-term forecast accuracy regarding its time-dependent feature. Besides, in this context, GAN is used for modeling sophisticated data distributions and generating realistic synthetic data, which would make the training more effective, hence enhancing robustness in general, especially when a few data points or noisy data are available. All these models would contribute to enhancing the accuracy and dependability of forecasting solar irradiance for better power administration and grid integration. The framework has a particular focus on this novel combination, which adds to the progress of solar irradiance prediction. It offers great potential for advancements in using renewable energy and making grids more stable. Analysis of the projected architecture reveals its superiority in predicting solar irradiance levels in different ways, owing to an MAE of 0.28 and RMSE of 0.35. Finally, as a result of comparative analysis with existing models like RNN, GRU, LSTM, and so on, the model excelled in all metrics, such as low errors and high R2. Overall, it is seen that the combined model always works better than other methods, which confirms its ability to give dependable and accurate short-term forecasts.