Contribution of meteorological factors based on explainable artificial intelligence in predicting wind farm power production using machine learning algorithms

Abstract

Wind fields are intermittent and nonlinear to meteorological factors and external environmental conditions. Statistical models have been proposed based on various approaches to precisely predict wind speed and energy production. However, determining the most suitable approach is challenging, regardless of the conditions. Currently, only wind speed, wind direction, temperature, atmospheric pressure, and humidity have been used as input features of models in most wind-power forecasting studies. However, few studies have described each feature's contribution to prediction performance when using meteorological factors, such as atmospheric stability and turbulence components, as input features. This study predicted the 10 min average power and daily energy production of a wind farm using four machine learning (ML) algorithms and 13 meteorological factors. The ultimate goal was to present the individual prediction contribution of meteorological factors using the Shapley additive explanations algorithm, which is an explainable artificial intelligence technique, based on the prediction results. Wind speed showed a dominant influence in the determination of energy production, followed by turbulent kinetic energy, turbulence intensity, and turbulence dissipation rate. Thus, insights into the detailed contribution of turbulence components to predict the performance facilitate the advancement of ML-based approaches, which can yield significant benefits in increasing the predictability of actual wind energy, thereby ensuring efficiency and stability in wind farm operations.