As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Random Forest Regressor, Linear Regression Model, Artificial Neural Network, k-Nearest Neighbors, Support Vector Regression, Gradient Boosting Regressor, Gaussian Process Regression, and Deep Learning, as to their use in forecasting direct solar radiation across six climatically diverse regions in the Kingdom of Saudi Arabia. The models were evaluated using eight statistical metrics along with time-series and absolute error analyses. A key contribution of this work is the introduction of Trigonometric Cyclical Encoding, which has significantly improved temporal representation learning. Comparative SHAP-based feature-importance analysis revealed that Trigonometric Cyclical Encoding enhanced the explanatory power of temporal features by 49.26% for monthly cycles and 53.30% for daily cycles. The findings show that Deep Learning achieved the lowest root mean square error, as well as the highest coefficient of determination, while Artificial Neural Network demonstrated consistently high accuracy across the sites. Support Vector Regression performed optimally but was less reliable in some regions. Error and time-series analyses reveal that Artificial Neural Network and Deep Learning maintained stable prediction accuracy throughout high solar radiation seasons, whereas Linear Regression, Random Forest Regressor, and k-Nearest Neighbors showed greater fluctuations. The proposed Trigonometric Cyclical Encoding technique further enhanced model performance by maintaining the overall fitness of the models, which ranged between 81.79% and 94.36% in all scenarios. This paper supports the effective planning of solar energy and integration in challenging climatic conditions.

The 2024 Africa Cup of Nations (AFCON) in Côte d’Ivoire highlighted the substantial energy demands of major sporting events, traditionally met by conventional, and nonrenewable sources. This study investigates the technoeconomic feasibility and environmental benefits of integrating solar and wind energy systems into the six stadiums used for the 2024 AFCON to enhance sustainability, long‐term economic benefits, and reduced carbon emissions. Utilizing the hybrid optimization of multiple electric renewables (HOMER) software for simulation and optimization, and the engineering equation solver for mathematical modeling, this study assesses the energy demand and potential renewable energy contributions for stadiums in Abidjan, Yamoussoukro, Bouaké, Korhogo, and San Pedro. The findings reveal that grid purchases dominate the energy mix across all cities, with varying contributions from solar and wind energy. Abidjan achieves 20.1% renewable energy penetration, while San Pedro reaches 69.9%. The proposed hybrid renewable energy systems offer substantial economic benefits, including payback periods of less than 2 years and high internal rates of return (IRR), with surplus energy generated in some locations potentially sold back to the grid. Additionally, the environmental impact assessment indicates an estimated annual CO ₂ reduction of 6518.25 metric tonnes cumulatively, equivalent to planting ~2.17 million trees over 20 years. This study provides a detailed technoeconomic analysis, demonstrating the viability of hybrid wind–solar systems in large sports venues and contributing valuable insights for future implementations of renewable energy technologies in the sports infrastructure sector.

Wind power forecasting is essential for the reliable and efficient operation of wind farms based on power grids, which is significantly important for stakeholders like wind farm owners, power pools, and power traders. Wind power prediction is also crucial for optimal power dispatch, grid security, and minimizing generation curtailment at wind farms. Hence, an accurate methodology for the power production of wind farms is needed for identifying long-term operational performance, failure detection, and ensuring grid integration. The present study proposes a hybrid machine learning (ML) approach that combines the strengths of random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVR), using linear regression (LR) as a meta-model for wind speed forecasting. This modeling approach was trained, validated, and tested on 87,600 hourly data points across 10 variables from high-wind potential sites in the Kingdom of Saudi Arabia: Damat Al Jandal, Taif, Abha, East Coast, and Red Sea. The key findings suggest that the hybrid RFR + ANN + SVR model performed better than individual models, including the simple persistence model, achieving R² scores up to 95.3 in testing, with train–test performance loss of less than 5% across all scenarios. The mean bias error (MBE) stayed within ±0.17 m/s, and the relative root mean square error (RRMSE) stayed below 15%. The hybrid model outperformed the metric-site standalone models and persistence model. Through feature importance analysis, this study also finds that temperature, relative humidity, and cyclical time-encoded features are the most important inputs. For offshore sites, thermal features like pressure and dew point were found to be more important. Due to its demonstrated superior performance across metric-site combinations, this hybrid framework is adaptable to other wind regions with intermittent renewables, with implications for reliable renewable energy integration and grid load stability.