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

TL;DR

This paper presents a new classification method for predicting wind ramp events that effectively handles severe class imbalance, improving accuracy and F1 scores in real-world datasets.

Contribution

It introduces a novel multivariate time series classification approach with data preprocessing and ensemble learning to address class imbalance in wind ramp event forecasting.

Findings

Achieves over 85% accuracy in real-world data

Attains 88% weighted F1 score, outperforming benchmarks

Demonstrates robustness 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.