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A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance

Alejandro Morales-Hernández, Fabrizio De Caroa, Gian Marco Paldino, Pascal Tribel, Alfredo Vaccaro, Gianluca Bontempi

Abstract

Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs). These early warnings enable the timely initiation of more detailed system stability assessments and preventive actions. However, forecasting these events is challenging due to the inherent class imbalance in WPRE datasets, where ramp events are less frequent (typically less than 15\% of observed events) compared to normal conditions. Ignoring this characteristic undermines the performance of conventional machine learning models, which often favor the majority class. This paper introduces a novel methodology for WPRE forecasting as a multivariate time series classification task and proposes a data preprocessing strategy that extracts features from recent power observations and masks unavailable ramp information, making it integrable with traditional real-time ramp identification tools. Particularly, the proposed methodology combines majority-class undersampling and ensemble learning to enhance wind ramp event forecasting under class imbalance. Numerical simulations conducted on a real-world dataset demonstrate the superiority of our approach, achieving over 85% accuracy and 88% weighted F1 score, outperforming benchmark classifiers.

A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance

Abstract

Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs). These early warnings enable the timely initiation of more detailed system stability assessments and preventive actions. However, forecasting these events is challenging due to the inherent class imbalance in WPRE datasets, where ramp events are less frequent (typically less than 15\% of observed events) compared to normal conditions. Ignoring this characteristic undermines the performance of conventional machine learning models, which often favor the majority class. This paper introduces a novel methodology for WPRE forecasting as a multivariate time series classification task and proposes a data preprocessing strategy that extracts features from recent power observations and masks unavailable ramp information, making it integrable with traditional real-time ramp identification tools. Particularly, the proposed methodology combines majority-class undersampling and ensemble learning to enhance wind ramp event forecasting under class imbalance. Numerical simulations conducted on a real-world dataset demonstrate the superiority of our approach, achieving over 85% accuracy and 88% weighted F1 score, outperforming benchmark classifiers.
Paper Structure (8 sections, 6 equations, 4 figures, 2 tables, 2 algorithms)

This paper contains 8 sections, 6 equations, 4 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. State of the Art
  3. Research Gap and Proposed Work
  4. Problem definition and data preparation
  5. Proposed approach
  6. Validation methodology
  7. Experimental results
  8. Conclusions

Figures (4)

  • Figure 1: Sample of the wind power time series adopted in this study. Five different events are highlighted with different colors to indicate the five different classes considered in the forecasting task.
  • Figure 2: Probability of ramp occurrence in the dataset
  • Figure 3: Confusion matrix of the classification results using EasyEnsemble with $l=4$ and (a) 3 classes and (b) 5 classes. The values were normalized over the true conditions (i.e. rows)
  • Figure 4: Feature relevance in EasyEnsemble computed through the Gini Importance or Mean Decrease in Impurity (MDI)