<p>In the modern energy world, the integration of renewable and non-renewable resources into the grid systems gives rise to many challenges due to their temporal natures. The prediction of energy using the intelligent model plays a vital role in addressing these challenges. Forecasting future energy needs and generation helps make national energy policies. Traditional forecasting models have often resulted from poor analysis and performance. Therefore, the current research has developed a novel Owl Radial Basis Forecasting (ORBF) method to predict the generation capacity based on energy needs using historical energy data. The research collected historical energy trading data, which was filtered for noise at the pre-processing layer of the proposed ORBF model. Further relevant features were extracted, and future energy values were predicted based on the owl search fitness process. The model's performance is analyzed using key metrics, including mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean squared error (MSE), coefficient of determination (R²), and computation time. The model provided a 0.0299 MSE, 0.0011 MAPE, 0.009 MAE, 0.152 RMSE, and an R² of 99.9%. The error values of the model are significantly lower compared to those of prevailing forecasting mechanisms. Additionally, the validated prediction time of the designed model is 10&#xa0;s.</p>