Intelligent approaches for global horizontal irradiance forecasting in Saudi Arabia: Benchmarking deep learning coupled with RMSprop optimization for solar power systems

• Estimation of hour-ahead global horizontal irradiance (GHI) by a newly hybrid AI model. • An integrated multilayer perceptron neural network (MPANN) and RMSprop optimization is established. • Models evaluated stepwise input reduction in GHI prediction to assess performance robustness. • RMSprop algorithm is applied for the hyperparameter optimization of the MPANN model. • MPANN- RMSprop had superior accuracy in global horizontal irradiance forecasting • The study presents a solid-structured case of solar resources assessment in Saudi Arabia. The Kingdom of Saudi Arabia (KSA) boasts a significant potential for solar power generation due to its abundant solar energy resources. This solar energy potential can reduce KSA’s reliance on oil, provide a reliable power supply, create new employment opportunities, and contribute to a cleaner environment. Hence, for the proper utilization and deployment of solar energy applications, a long-term statistical understanding of the availability of sunshine intensity is critical, both in temporal and spatial domains. Accurate forecasting of solar resources is essential for effective integration of photovoltaics into the national grid. This study presents a hybrid framework integrating Multilayer Perceptron Artificial Neural Network (MPANN) and RMSprop Optimization (RMSO) for estimating the hour-ahead Global Horizontal Irradiance (GHI) for six locations in Saudi Arabia: Jubail, Al Jouf, Hagl, Riyadh, Jedda, and Najran. RMSO is employed to determine the optimal selection of MPANN hyperparameters to maximize prediction accuracy. The models analyse the stepwise reduction of input parameters in GHI prediction models to assess performance under diversified input feature sets. WindGHI-4 includes four input parameters: wind speed, air temperature, hour of the day, and previous-hour GHI. TempGHI-3 excludes wind speed, using three inputs, while LiteGHI-2 further simplifies the inputs to only air temperature and the hour of the day. The findings indicated the hybrid MPANN-RMSO model based WindGHI-4 features outperformed TempGHI-3 and LiteGHI-2 for GHI forecasting. The simulated results revealed that the deterministic coefficient is estimated as 0.993-0.983 for WindGHI-4, compared to 0.990-0.980 for TempGHI-3, and 0.972 and 0.905 for LiteGHI-2, respectively, for GHI prediction at six different zones in Saudi Arabia. The findings enable high-accuracy, hour-ahead GHI forecasting across diverse Saudi climatic conditions, providing robust decision support for solar power integration, improved grid stability, and more reliable operational planning.