Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence

Abstract

The integration of wind power into the electricity grid faces a significant challenge due to the unpredictable nature of wind speed fluctuations. Therefore, ensuring precise short-term predictions of power output from wind turbines is vital for effectively incorporating wind energy into the grid and proficiently managing power systems. In this paper, an Artificial Neural Network (ANN)-based approach for the short-term power forecasting of wind turbines based on a swarm intelligence algorithm is proposed. Also, a simulation study of the wind power real system at different wind speeds is presented by using MATLAB/Simulink. The swarm intelligence algorithm is employed to optimize the forecasting model parameters. The performance of the proposed algorithm is evaluated using real data from a wind farm in Turkey. Three distinct methodologies are utilized to process the data efficiently: ANN, ANN with Firefly Algorithm (ANN-FA), and ANN with Particle Swarm Optimization (ANN-PSO). The results demonstrate that the swarm intelligence algorithm outperforms traditional forecasting methods, such as statistical approaches and machine learning techniques, in terms of accuracy and reliability. Furthermore, the computational efficiency of the algorithm is examined, and it is shown that the swarm intelligence-based approach offers a practical solution for real-time forecasting applications. The algorithm’s scalability and adaptability make it suitable for large-scale wind farms with multiple turbines, as it can handle the inherent variability and uncertainties associated with wind power generation. The proposed method offers an accurate and reliable forecasting tool that can assist power system operators and energy market participants in making informed decisions for the efficient utilization of wind energy resources.