Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?

With the increasing amount of wind and solar energy fed into the European power

grid, despite slowdowns due to social acceptance and regulatory issues, plus the

transition to a fossil fuel-free energy production, accurate predictions including

uncertainties are required for grid operators. Relying heavily on renewable energy

sources, frequently updated and as accurate as possible predictions for both

high-resolution temporal and spatial scales ensure grid management and taking

prevention measures in case of extreme meteorological events affecting the power

production. Moreover, both extreme events in weather across the nowcasting to weeks ahead

time scale and combined non-necessarily extreme weather factors can notably affect the power production grid. The latter is sometimes a combination of just-above-normal events, such as  high solar penetration, high wind penetration plus

decent hydropower combined with a reduced electricity demand. Thus, on-demand

available predictions of the expected power production are especially needed. Post-processing

methods enable targeted forecasts of meteorological parameters at site-location and

regional level, serving as a baseline for the conversion to power production,

particularly a direct conversion of NWP predictions and observations to power

production.

 

 

However, so far most methods available use NWP forecast with spatial resolutions

between 2 and 9 km, and hourly output frequency thus requiring temporal interpolation and

spatial downscaling or point interpolation. Recently, NWP upgrades

towards sub-km scale and sub-hourly have been done to improve extreme events prediction. These NWP models are only

available for selected extremes and short periods of time and often do not share the

same parametrization throughout. This poses a challenge for post-processing for

renewables, as well as the uncertainties that lie in e.g. wind farm

specifications and solar farm/PV panels. To prepare for the next phase of NWP

models, and currently developing on-demand extreme digital twin forecasting system, fast and

transferable post-processing methods are needed. Here, we look into different

machine learning and classical statistical methods, such as the analog method,

LSTMs and Random forest, EMOS, to generate post-processed forecasts for extreme

events with a sparse training database. Furthermore, we investigate the

transferability and generalisability of these methods when pre-trained with a

coarser NWP model.