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

Probabilistic forecasting of electricity load curves is of fundamentalimportance for effective scheduling and decision making in the increasinglyvolatile and competitive energy markets. We propose a novel approach toconstruct probabilistic predictors for curves (PPC), which leads to a naturaland new definition of quantiles in the context of curve-to-curve linearregression. There are three types of PPC: a predictive set, a predictive bandand a predictive quantile, all of which are defined at a pre-specified nominalprobability level. In the simulation study, the PPC achieve promising coverageprobabilities under a variety of data generating mechanisms. When applying toone day ahead forecasting for the French daily electricity load curves, PPCoutperform several state-of-the-art predictive methods in terms of forecastingaccuracy, coverage rate and average length of the predictive bands. Thepredictive quantile curves provide insightful information which is highlyrelevant to hedging risks in electricity supply management.

Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression