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Poster
in
Workshop: Tackling Climate Change with Machine Learning

Towards turbine-location-aware multi-decadal wind power predictions with CMIP6

Nina Effenberger · Nicole Ludwig

[ ]
 
presentation: Tackling Climate Change with Machine Learning
Sun 15 Dec 8:15 a.m. PST — 5:30 p.m. PST

Abstract:

With the increasing amount of renewable energy in the grid, long-term wind power forecasting for multiple decades becomes more critical. In these long-term forecasts, climate data is essential as it allows us to account for climate change. Yet the resolution of climate models is often very coarse. In this paper, we show that by including turbine locations when downscaling with Gaussian Processes, we can generate valuable aggregate wind power predictions despite the low resolution of the CMIP6 climate models. This work is a first step towards multi-decadal turbine-location-aware wind power forecasting using global climate model output.

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