A physics-inspired neural network model for short-term wind power prediction considering wake effects

Accurate short-term wind power prediction plays an essential role in the wind farm control and the dispatch of wind energy into the power system. Incorporating physical factors that have a major impact on wind farm power generation into machine learning algorithms has always been an important way to improve prediction accuracy. Overlooked in the literature, however, is the influence of wind turbines wakes in improving model predictions. In this work, a physics-inspired neural network model for short-term wind power prediction is developed considering wake effects. Different from traditional neural network models, part of the nodes in the proposed model are determined by the analytical wake model, which enhances the statistical prediction model physically. In this way, the model can be well adapted to the wake effects in the wind farm. Verifications in the actual wind farm case illustrate that there is a good agreement with the prediction results and measured data. Compared with traditional models, the wind power prediction performance of the proposed model has improved by more than 20% in terms of RMSE. Based on this work, we recommend that the wake effect should be considered in the short-term wind power prediction model, which is of great benefit to improving its accuracy.

Automated summary

Accurate short-term wind power prediction plays a vital role in wind farm control and the integration of wind energy into the power system. Incorporating wake effects into a physics-inspired neural network model improves the accuracy of wind power prediction by over 20% compared to traditional models. Considering wake effects is recommended for enhancing the accuracy of short-term wind power prediction.