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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ramp events are large rapid variations within wind power time series. Ramp forecasting canbenefit from specific strategies so as to particularly take into account these shifts inthe wind power output dynamic. In the short-term context (characterized by predictionhorizons from minutes to a few days), a Regime-Switching (RS) model based on ArtificialNeural Nets (ANN) is proposed. The objective is to identify three regimes in the windpower time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessmentmethodology is also proposed, based on a local gradient criterion. The RS-ANN model iscompared to a single-ANN model (without regime discrimination), concluding that theregime-switching strategy leads  to significant improvements for one-hour ahead forecasts,mainly due to the improvements obtained during ramp-up events. Including other explanatoryvariables (NWP outputs, local measurements) during the regime assessment could eventuallyimprove forecasts for further horizons

Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks