Optimization of Seasonal Wind Energy Forecasts with PSO Algorithm

Abstract

Today, due to the depletion of fossil fuels and increasing greenhouse gas emissions, the expansion of renewable energy sources has accelerated. The generation of electricity from wind energy is an important component of renewable energy sources, and the electricity generated is highly affected by seasonal variations. The intermittent nature of wind energy requires optimization of power generation, grid operations and maintenance activities. Accessing the seasonal energy forecasts of wind turbines with the highest accuracy is of utmost importance for this optimization. The aim of this study is the optimization of the seasonal energy and power prediction of wind turbines by means of advanced metaheuristic techniques. The Particle Swarm Optimization (PSO) algorithm, which has already been proven in the literature, is proposed as a possible solution. The effectiveness of the PSO algorithm is tested in a case study and its ability to improve energy production forecasts for a wind power generation plant located in the Central Anatolia region of Türkiye is demonstrated. Findings of this study show that the PSO algorithm can improve the accuracy of wind turbine power output prediction, thereby increasing the efficiency of wind power plants and reducing maintenance costs.