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The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece
Open AccessArticle

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

1
Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, 1180 Vienna, Austria
2
LNEG—The National Laboratory for Energy and Geology, Resource Economics Unit, 1649-038 Lisbon, Portugal
3
MINES ParisTech, PSL Research University, O.I.E. Centre Observation, Impacts, Energy, 06904 Sophia Antipolis, France
*
Author to whom correspondence should be addressed.
Energies 2020, 13(9), 2277; https://doi.org/10.3390/en13092277
Received: 24 February 2020 / Revised: 9 April 2020 / Accepted: 30 April 2020 / Published: 5 May 2020
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. View Full-Text
Keywords: wind power simulation; wind power time series; reanalysis; machine learning wind power simulation; wind power time series; reanalysis; machine learning
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Baumgartner, J.; Gruber, K.; Simoes, S.G.; Saint-Drenan, Y.-M.; Schmidt, J. Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja. Energies 2020, 13, 2277.

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