Machine Learning for Wind Power Prediction. A Comparative Analysis of Traditional Machine Learning Models and Graph Neural Network for Wind Power Prediction and Forecasting in Wind Farms

Abstract

This thesis presents a comparative study of machine learning (ML) models and the deep learning (DL) model graph neural network (GNN) for wind power prediction and short- to medium-term forecasting in wind farms. Using high-resolution SCADA data from a 16-turbine onshore wind farm in Sweden, along with re-analysis and forecast weather datasets, various models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), k-nearest neighbours (kNN), Multi-Layer Perceptron (MLP), and GNN were trained and evaluated. Two baseline approaches, the farm’s theoretical power curve and FLORIS wake model, were used as references. Results show that ML models outperform baseline models in predicting wind power output, with GNN achieving the best overall performance, although all ML models perform similarly. The ability of the models to generalize from wind power prediction to forecasting is however limited. The findings indicate that re-analysis data with low spatial resolution fails to adequately capture local weather conditions necessary for accurate power prediction. The study also investigates the effects of input feature selection, temporal resolution, and multi-task learning on model performance. Furthermore, it identifies challenges related to input data quality, particularly in the estimation of global wind conditions from SCADA-based measurements. These results underscore the potential of ML methods for wind power applications and highlight the critical importance of accurately representing global weather data, as well as accounting for discrepancies between training data and forecast data.