Hybrid Cuckoo Search and Genetic Algorithm for Optimizing Electricity Forecast

The optimization of electricity forecasts is crucial for enhancing the efficiency and performance of power systems. This paper presents a novel hybrid approach that integrates the Cuckoo Search Algorithm (CSA) and Genetic Algorithm (GA) to optimize electricity forecasting. The CSA, inspired by the breeding behavior of cuckoo birds, excels in global exploration, while the GA, based on natural evolution principles, provides effective local search and exploitation capabilities. By synergistically combining the strengths of both algorithms, the proposed hybrid approach aims to improve the accuracy, convergence speed, and robustness of optimization processes in electricity forecasting. This CSA-GA fusion technique leverages the CSA's efficient exploration and the GA's refinement capabilities to offer enhanced solutions for electricity forecast optimization. Experimental evaluations conducted on real-world electricity datasets from Luzon, Visayas, and Mindanao grid data of the Philippines demonstrate the effectiveness and superiority of the hybrid approach, showcasing its ability to achieve improved performance and obtain near-optimal solutions in various electricity data analysis tasks.