Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithms. LSTM model produces predictive signals that help inform the search trajectory of CS, enabling better exploration–exploitation tradeoff of resource scheduling on uncertainty. Results Simulation experiments on benchmark renewable energy datasets showed that ML-HCS not only converges 12% faster than the best of the GA, PSO, and classical CS, but also achieves 7–10% better quality of solutions and 9% higher robustness. This model also adapted better in multi-objective optimization tasks: cost minimization, scheduling stability and prediction accuracy. Conclusions Finally, the ML-HCS framework provides a prediction-oriented, data-driven, scalable optimization methodology for renewable energy systems. Its use of machine learning and metaheuristic search provide for high forecasting accuracy and resiliency in operation, which will enable its future large scale smart grid and renewable energy management applications.
