A machine learning approach for wind turbine power forecasting for maintenance planning

Integrating power forecasting with wind turbine maintenance planning enables an innovative, data-driven approach that maximizes energy output by predicting periods low wind production and aligning them with maintenance schedules, improving operational efficiency. Recently, many countries have met renewable energy targets, primarily using wind and solar, to promote sustainable growth and reduce emissions. Forecasting wind turbine power production is crucial for maintaining a stable and reliable power grid. As renewable energy integration increases, precise electricity demand forecasting becomes essential at every power system level. This study presents and compares nine machine learning (ML) methods for forecasting, Interpretable ML, Explainable ML, and Blackbox model. The interpretable ML includes Linear Regression (LR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Random Forest (RF); the explainable ML consists of graphical Neural network (GNN); and the blackbox model includes Multi-layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These methods are applied to the EDP datasets using three causal variable types: including temporal information, metrological information, and power curtailment information. Computational results show that the GNN-based forecasting model outperforms other benchmark methods regarding power forecasting accuracy. However, when considering computational resources such as memory and processing time, the XGBoost model provides optimal results, offering faster processing and reduced memory usage. Furthermore, we present forecasting results for various time windows and horizons, ranging from 10 minutes to a day.