Wind speed prediction using ordinal classification: an analysis of extreme values

Wind speed forecasting represents a significant challenge in the global transition to sustainable energy systems. Wind energy, characterised by zero greenhouse gas emissions and relatively low cost, is a renewable resource that depends heavily on meteorological conditions, which are inherently variable and unpredictable. This variability and intermittency present substantial obstacles to ensuring a consistent power supply, underscoring the importance of accurate wind speed prediction as a critical area of research. Among the various approaches explored to address this challenge, machine learning (ML) has emerged as a prominent solution. ML includes methodologies such as regression (predicting continuous values of wind speed) and nominal classification (predicting discrete categories of wind speed). In nominal classification, wind speeds are discretised into classes to provide essential information for wind farm operations. In this study, wind speeds are categorised into four classes: 1) very low speeds, 2) moderate speeds, 3) high speeds, and 4) extreme wind speeds. While both very low and extreme speeds result in no power generation, this work focuses on the extreme wind speed class, as these events often necessitate turbine shutdowns to prevent structural damage.

To address the challenges of wind speed forecasting with a focus on extreme wind events, we propose the use of ordinal classification, a ML paradigm specifically designed for tasks where output categories exhibit a natural order, as is the case in this work. This study evaluates hourly wind speed predictions for a wind farm in Spain, using data collected over more than 15 years. Additionally, input features include meteorological variables such as temperature, wind components (u and v), and sea level pressure, among others. Forecasts are generated for three time horizons (1h, 4h, and 8h) to provide sufficient lead time for mitigating risks associated with extreme wind conditions. Two ordinal classification models based on artificial neural networks (ANNs) are analysed: 1) an ANN coupled with the cumulative link model (CLM), and 2) an ANN using a soft labelling optimisation technique. Additionally, other competitive ordinal and nominal classification methods are included for comparative analysis.

The results demonstrate that the proposed models outperform a number of nominal and ordinal classification methods. The ANN coupled with CLM delivers superior overall performance across all four classes, while the ANN employing the soft labelling approach achieves higher accuracy in predicting extreme wind speed events. These findings underscore the potential of ordinal classification to enhance wind speed forecasting, contributing to more effective wind farm management and the broader integration of renewable energy sources.