Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power

TL;DR

This paper introduces a novel neural network approach with a smooth pinball loss for probabilistic wind power forecasting, improving quantile estimates and avoiding quantile crossing issues.

Contribution

It presents a new nonparametric neural network method combining a smooth pinball loss and a weighting scheme to enhance probabilistic wind power forecasts.

Findings

Outperforms benchmark models in quantile score and reliability.

Effectively prevents quantile crossing in probabilistic forecasts.

Provides accurate prediction intervals for wind power.

Abstract

Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources 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 a novel approach for nonparametric probabilistic forecasting of wind power that combines a smooth approximation of the pinball loss function with a neural network architecture and a weighting initialization scheme to prevent the quantile cross over problem. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. Multiple quantiles are estimated to form 10%, to 90% prediction intervals which are evaluated using a quantile score and reliability measures. Benchmark models such as the persistence and climatology distributions, multiple quantile regression, and support vector quantile regression are used for comparison where results demonstrate the proposed approach leads to improved performance while preventing the problem of overlapping quantile estimates.