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Integrating photovoltaic power into the power system can offer significant economic and environmental benefits. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the current power system's planning and operation. Accurate photovoltaic power generation forecasting is crucial for delivering high-quality electric energy to consumers and increasing system reliability.In this study, a multi-stage approach is proposed. In the first stage, three decomposition techniques (Iterative Filtering decomposition, Variational Mode Decomposition, and Wavelet Packet Decomposition) are employed for time series decomposition. Then, for each Intrinsic Mode Function (IMF) component resulting from the decomposition block, five machine learning and three deep learning algorithms are utilized, serving as local forecasting models. In the final forecast phase, the best forecasting result for each regressor is selected during the reconstruction phase. Two years of photovoltaic power data recorded in three grid-connected photovoltaic systems installed in South Algeria were utilized for training and testing the proposed forecasting models. Upon comprehensive analysis and examination of the outcomes, the proposed method exhibits the lowest normalized Root Mean Square Error values across all forecast horizons and monitoring stations. Particularly, for forecasting steps at time intervals +1, +3, and +5, the proposed method attains an average normalized Root Mean Square Error, showcasing its efficacy: 0.709%, 2.097%, and 3.241% for station 1; 1.147%, 3.546%, and 5.347% for station 2; and 0.922%, 2.158%, and 4.539% for station 3. The experimental results underscore the superiority of our approach over conventional regression algorithms, thus substantiating its prowess in delivering robust and competitive performance outcomes. [ABSTRACT FROM AUTHOR]

• •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]