Solar Irradiation Forecasting Using Genetic Algorithms

<ns7:p>Background For many applications such as photovoltaic system planning, grid stability, and renewable energy integration, accurate forecasting of solar irradiation is essential. Solar irradiation exhibits strong nonlinearity and variability driven by atmospheric conditions, seasonal cycles, and site-specific meteorological factors, making reliable short-term prediction challenging for conventional statistical models. Methods We propose a hybrid solar irradiation forecasting framework that combines machine learning regression models with genetic algorithm (GA)–based model optimization. Multiple predictive models, including linear regression and gradient-boosted decision trees, are trained using meteorological variables such as zenith angle, temperature, relative humidity, and atmospheric indices. The GA is employed to identify optimal feature subsets by maximizing predictive accuracy while minimizing redundancy, treating feature selection as a combinatorial optimization problem. Model performance is evaluated using mean absolute error (MAE), variance, and percentage accuracy derived from mean absolute percentage error (MAPE). Results The GA-optimized models demonstrate improved forecasting accuracy and stability compared to baseline machine learning approaches using full feature sets. Empirical results show that GA-based feature selection reduces prediction error, enhances robustness across seasonal regimes, and yields interpretable feature importance patterns aligned with known physical drivers of solar irradiation. Comparative evaluation across training and validation datasets confirms consistent gains in predictive performance. Conclusions The proposed genetic algorithm–enhanced forecasting framework provides an effective and interpretable approach for modeling solar irradiation under complex atmospheric conditions. By integrating evolutionary optimization with machine learning, the methodology supports more reliable solar energy forecasting and can be readily extended to real-time energy management systems and large-scale renewable deployment scenarios.</ns7:p>