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

2026-03-20

AI Summary

This study addresses the challenge of predicting wind power ramping events, which are critical for grid stability yet difficult to model due to severe class imbalance—such events typically constitute less than 15% of observations. Framing the task as a multivariate time series classification problem, the work proposes a direct classification framework tailored for extreme imbalance scenarios. The approach integrates feature extraction with masking of unavailable information during preprocessing, followed by undersampling of the majority class and ensemble learning to substantially enhance minority-class detection. The resulting model seamlessly integrates into existing real-time ramp identification systems and achieves over 85% accuracy and an 88% weighted F1-score on real-world datasets, significantly outperforming baseline methods.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Wind Power Ramp Events
• Class Imbalance
• Forecasting
• Grid Stability
• Decision Support Systems

Innovation

• wind power ramp events
• class imbalance
• time series classification
• ensemble learning
• undersampling