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

TL;DR

This study demonstrates that machine learning models can generate long-term wind power time series with comparable or superior quality to traditional models like Renewables.ninja, even without detailed location data, aiding renewable energy planning.

Contribution

The paper introduces neural network-based models that produce high-quality wind power time series without requiring detailed location information, outperforming or matching existing methods.

Findings

ML models match or outperform Renewables.ninja in replicating observed wind power data.

ML models can generate accurate time series with less location-specific information.

ML models effectively capture extreme events and power ramps.

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 as well as for bias correction, and their insufficient replication of extreme events and short-term power ramps. We assess how time series generated by machine learning models (MLM) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we apply neural networks to one MERRA2 reanalysis wind speed input dataset with no location information and one with basic location information. The resulting time series and the RN time series are compared with actual generation. Both MLM time series feature equal or even better time series quality than RN depending on the characteristics considered. We conclude that MLM models can, even when reducing information on turbine locations and turbine types, produce time series of at least equal quality to RN.