Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja

Members of the Clim2power consortium have published a new article on the journal Energies


Image caption: Scatterplot of modelled generation time series compared with observations (Renewables.ninja time series abbreviated as RN and machine learning model time series abbreviated as MLM1 and MLM2 versus observations on the X-axis)

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve-based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation, despite their need for accurate location information and bias correction, as well as their insufficient replication of extreme events and short-term power ramps. In this paper, we assessed how time series generated by machine learning models (MLMs) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we applied neural networks to one wind speed input dataset derived from MERRA2 reanalysis with no location information and two with additional location information. The resulting time series and RN time series were compared with actual generation. All MLM time series feature an equal or even better time series quality than RN, depending on the characteristics considered. We conclude that MLM models show a similar performance to RN, even when information on turbine locations and turbine types is unavailable

The full article is available through this link: https://www.mdpi.com/1996-1073/13/9/2277

Authors: Baumgartner, Johann; Gruber, Katharina; Simoes, Sofia, Saint-Drenan, Yves-Marie and Schmidt, Johannes

JPI Climate Central Secretariat, Tuesday 19 May 2020