Enhancing direct Normal solar Irradiation forecasting for heliostat field applications through a novel hybrid model.

• •Development of an enhanced hybrid model for multi-hour ahead DNI forecasting. • •Comprehensive performance analysis using four different DNI databases. • •Implementation of the GA-GOA Algorithm for CSP Optimization. • •Assessment of electricity generation from CSP plants for multi-hour ahead forecasting. This study addresses the critical need for precise Direct Normal Irradiation forecasting in concentrating solar power systems to enhance performance and manage power generation intermittency. We propose a novel hybrid model that combines Variation Mode Decomposition, Swarm Decomposition Algorithm, Random Forest for feature selection, and Deep Convolutional Neural Networks, aiming to improve the forecasting accuracy. This model covers the entire process from Direct Normal Irradiation forecasting to heliostat field optimization and electricity generation. We validated the model across four globally diverse regions, taking into account their distinct climates and meteorological conditions. The results show that our model aligns closely with actual measurements and outperforms existing forecasting methods in terms of precision and stability. The forecasting performance was assessed using normalized Root Mean Square Error, with results ranging from 0.75% to 3.4% across different regions. This demonstrates the model's potential for real-world application in concentrating solar power systems, optimizing heliostat field effectiveness, and reliably forecasting electricity production for grid management. [ABSTRACT FROM AUTHOR]