An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power

TL;DR

This paper evaluates a nonparametric probabilistic wind power forecasting method combining support vector machines with quantile regression, demonstrating improved accuracy and non-crossing quantile estimates in a case study.

Contribution

It introduces a novel support vector quantile regression approach with non-crossing constraints for wind power forecasting, outperforming benchmark models.

Findings

Proposed method achieves lower pinball loss than benchmarks.

Effectively prevents crossing of quantile estimates.

Provides reliable prediction intervals for wind power.

Abstract

Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing constraints. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 20%, 40%, 60% and 80% prediction intervals which are evaluated using the pinball loss function and reliability measures. Three benchmark models are used for comparison where results demonstrate the proposed approach leads to significantly better performance while preventing the problem of overlapping quantile estimates.