Machine Learning Assisted Hybrid Cuckoo Search for Predictive Optimization in Renewable Energy Systems

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Computational Optimization

Background:

• Renewable energy sources (RES) like solar and wind exhibit intermittent and uncertain behavior, challenging grid stability.
• Traditional optimization methods struggle with large-scale, dynamic, and stochastic renewable energy environments due to their NP-hard nature.

Purpose of the Study:

• To develop an advanced optimization framework for renewable energy systems (RES).
• To enhance forecasting accuracy and operational resiliency in smart grids.
• To address the limitations of traditional methods in managing complex RES.

Main Methods:

• Introduction of the Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) framework.
• Integration of Long Short-Term Memory (LSTM) networks for predictive forecasting.
• Combination of LSTM regression models with hybrid optimization algorithms for resource scheduling under uncertainty.

Main Results:

• ML-HCS converges 12% faster than traditional algorithms (GA, PSO, CS).
• Achieves 7-10% better solution quality and 9% higher robustness.
• Demonstrates superior performance in multi-objective tasks including cost minimization, scheduling stability, and prediction accuracy.

Conclusions:

• ML-HCS offers a scalable, data-driven optimization methodology for RES.
• The framework leverages machine learning and metaheuristic search for high forecasting accuracy and operational resilience.
• Enables future large-scale smart grid and renewable energy management applications.