English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Uncertainty analysis in the form of probabilistic forecasting can providesignificant improvements in decision-making processes in the smart power gridfor better integrating renewable energies such as wind. Whereas pointforecasting provides a single expected value, probabilistic forecasts providemore information in the form of quantiles, prediction intervals, or fullpredictive densities. This paper analyzes the effectiveness of an approach fornonparametric probabilistic forecasting of wind power that combines supportvector machines and nonlinear quantile regression with non-crossingconstraints. A numerical case study is conducted using publicly available winddata from the Global Energy Forecasting Competition 2014. Multiple quantilesare estimated to form 20%, 40%, 60% and 80% prediction intervals which areevaluated using the pinball loss function and reliability measures. Threebenchmark models are used for comparison where results demonstrate the proposedapproach leads to significantly better performance while preventing the problemof overlapping quantile estimates.

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