Enhancing wind speed prediction in wind farms through ordinal classification

This paper presents and evaluates two novel ordinal classification methods for wind speed prediction, considering three prediction time-horizons: 1h, 4h, and 8h. To address the problem, wind speed values are discretised into four classes, critical for wind farm management. Each class represents essential information for wind farm production, ranging from very low wind speeds to extreme wind speed events and the corresponding production conditions, facilitating operational decisions for wind farm operators. Ordinal classifiers are more suitable than nominal methods to tackle this problem. The study’s primary objective is to compare recently proposed ordinal classifiers for addressing the challenges of wind speed prediction with a focus on extreme wind conditions, which are responsible for many turbine shutdowns. Hourly wind speed measurements from a Spanish wind farm and predictor variables from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5 Reanalysis) model are used. The proposed methods include an Artificial Neural Network (ANN) model implementing the Cumulative Link Model as an ordinal output function (MLP-CLMO), which emphasises overall performance, and an ANN model optimised using a soft labelling technique based on triangular distributions (MLP-TO), which excels at handling extreme class performance. The results demonstrate the superiority of both approaches over other nominal and ordinal methods across performance metrics that account for the unbalanced nature and ordinality of the data. MLP-CLMO excels in overall and ordinal performance, while MLP-TO demonstrates superior handling of the extreme class predictions.