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

Driven by climatic processes, wind power generation is inherently variable.Long-term simulated wind power time series are therefore an essential componentfor understanding the temporal availability of wind power and its integrationinto future renewable energy systems. In the recent past, mainly power curvebased models such as Renewables.ninja (RN) have been used for derivingsynthetic time series for wind power generation despite their need for accuratelocation information as well as for bias correction, and their insufficientreplication of extreme events and short-term power ramps. We assess how timeseries generated by machine learning models (MLM) compare to RN in terms oftheir ability to replicate the characteristics of observed nationallyaggregated wind power generation for Germany. Hence, we apply neural networksto one MERRA2 reanalysis wind speed input dataset with no location informationand one with basic location information. The resulting time series and the RNtime series are compared with actual generation. Both MLM time series featureequal or even better time series quality than RN depending on thecharacteristics considered. We conclude that MLM models can, even when reducinginformation on turbine locations and turbine types, produce time series of atleast equal quality to RN.

Machine learning models show similar performance to Renewables.ninja for  generation of long-term wind power time series even without location  information