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

Johann Baumgartner, Katharina Gruber, Sofia G. Simoes, Yves-Marie Saint-Drenan and Johannes Schmidt
Additional contact information
Johann Baumgartner: Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
Katharina Gruber: Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
Sofia G. Simoes: LNEG—The National Laboratory for Energy and Geology, Resource Economics Unit, 1649-038 Lisbon, Portugal
Yves-Marie Saint-Drenan: MINES ParisTech, PSL Research University, O.I.E. Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
Johannes Schmidt: Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, 1180 Vienna, Austria

Energies, 2020, vol. 13, issue 9, 1-23

Abstract: 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.

Keywords: wind power simulation; wind power time series; reanalysis; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/9/2277/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/9/2277/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:9:p:2277-:d:354110

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().