Application of four machine-learning methods to predict short-horizon wind energy

Abstract

Renewable energy has garnered attention due to the need for sustainable energy sources. Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy. As the importance of wind energy grows, it can be crucial to provide forecasts that optimize its performance potential. Artificial intelligence (AI) methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction. This study explored the area of AI to predict wind-energy production at a wind farm in Yalova, Turkey, using four different AI approaches: support vector machines (SVMs), decision trees, adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANNs). Wind speed and direction were considered as essential input parameters, with wind energy as the target parameter, and models are thoroughly evaluated using metrics such as the mean absolute percentage error (MAPE), coefficient of determination (R²), and mean absolute error (MAE). The findings accentuate the superior performance of the SVM, which delivered the lowest MAPE (2.42%), the highest R² (0.95), and the lowest MAE (71.21%) compared with actual values, while ANFIS was less effective in this context. The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms, such as metaheuristic algorithms.