Advanced solar radiation prediction using combined satellite imagery and tabular data processing.

Accurate solar radiation prediction is crucial for optimizing solar energy systems. There are two types of data that can be used to predict solar radiation, such as satellite images and tabular satellite data. This research focuses on enhancing solar radiation prediction by integrating data from two distinct sources: satellite imagery and ground-based measurements. By combining these datasets, the study improves the accuracy of solar radiation forecasts, which is crucial for renewable energy applications. This research presents a hybrid methodology to predict the solar radiation from both satellite images and satellite data. The methodology basis on two datasets; the first data set contains tabular data, and the second dataset contains satellite images. The framework divides into two paths; the first path take the input as the satellite images; this stages contains three steps; the first step is removing noise using latent diffusion model, the second step is about pixel imputation using a modified RF + Identity GAN (this model contains two modification the first modification is adding the identity block to solve mode collapse problem in the GANs and the second modification is to add the 8-connected pixel to generate a value of missing pixel near to the real missed pixel. The third step in the first path is about using the self-organizing map to identify the special informative in the satellite image. The second path take the input as tabular data and use the diffusion model to impute the missing data in the tabulated data. Finally, we merge the two path and use feature selection to be as input for the LSTM for solar radiation predictions. The experiments done prove the efficiency of the used stage such as missing pixel imputation, removing noise, missing data imputation and prediction using LSTM when compared with other available techniques. The experiments also prove the enhancement of all prediction model after adding two paths before the prediction step.