Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach

Abstract

Wind energy, an essential component in the fight against climate change, relies heavily on precise, detailed wind field simulations to optimize wind farms, particularly in complex terrain with intricate wind patterns. However, conventional high-resolution simulations come with a hefty computational cost, limiting their applicability in real-time decision-making. This research addresses this challenge by proposing a machine learning-driven, computationally efficient approach utilizing a modified Generative Adversarial Network. The contribution of this work is threefold. Firstly, we provide access to a unique high-resolution dataset of wind fields in complex terrain. Secondly, we introduce a knowledge-based modification to the loss function, ensuring that the algorithm captures crucial characteristics of the flow within complex terrains. Finally, we demonstrate the potential of our approach to enhance wind flow resolution in real-life wind farms. Through this, our method delivers comparable accuracy to high-resolution simulations while substantially reducing computational demands. This advancement greatly enhances the accessibility and efficiency of high-resolution wind field simulations, facilitating real-time optimization of wind farms. Moreover, we illustrate that by designing an appropriate loss function informed by domain knowledge, we can mitigate the need for adversarial training.