Wind ramp event prediction with parallelized Gradient Boosted Regression Trees

TL;DR

This paper introduces a parallelized gradient boosted regression tree method for accurately classifying wind ramp events, including rare extremes, to improve power system reliability with high wind energy integration.

Contribution

It presents a novel parallelized gradient boosted regression tree approach for wind ramp event classification, demonstrating improved accuracy over existing benchmarks.

Findings

Superior classification accuracy compared to benchmarks

Effective detection of rare extreme wind events

Validated on NREL wind power data

Abstract

Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy. This paper proposes a classification based approach for estimating the future class of wind ramp event based on certain thresholds. A parallelized gradient boosted regression tree based technique has been proposed to accurately classify the normal as well as rare extreme wind power ramp events. The model has been validated using wind power data obtained from the National Renewable Energy Laboratory database. Performance comparison with several benchmark techniques indicates the superiority of the proposed technique in terms of superior classification accuracy.