Data-Driven Weather Scenario Generation for Long-Term Energy System Planning in the Nordic Region

Long-term energy system planning requires realistic weather scenarios that capture both short-term variability and long-term climate statistics, as well as rare but high-impact events. By preserving spatial and inter-variable correlations, we ensure robust multi-year energy market modelling in systems with large storage capacities, such as the Nordic power market. 

Current weather scenarios are based on ERA5 (Hersbach et al., 2020), where a period of 20 years (2003-2022) is used to establish synthetic weather scenarios eriod of 20 years (2003-2022) is used to establish synthetic weather scenarios (Martino et al., 2017). These weather scenarios consist of real weather but stitched together by different segments of 10 days pulled from the 20 years of samples.  Several statistical techniques, including quantile mapping, are applied during this process. However, this pipeline can introduce unphysical results and is both complex and time-consuming. In contrast, data-driven models offer a cost-effective solution for generating long-term forecasts efficiently.

In this study, the WeatherGenerator is employed to generate year-long independent weather scenarios by running the model under varying initial conditions. The analysis focuses on the Nordic region, where we evaluate the capability of the WeatherGenerator to reproduce long-term climate statistics for key variables. 

Its performance is benchmarked against weather scenarios produced by current in-house methodology and potentially alternative data-driven models such as AIFS or Bris.

Note: The WeatherGenerator project (grant agreement No101187947) is funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the Commission. Neither the European Union nor the granting authority can be held responsible for them.

 

(Hersbach et al., 2020) H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, and J.-N. Thépaut, “The ERA5 global reanalysis,” Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999– 2049, 2020

(Martino et al., 2017) S. Martino, T. N. Nipen, C. Lussana and S. Kolberg “A stochastic weather generator based on resampling historical ensemble weather forecasts and its application to hydrological simulation”, 2017, SINTEF Energi AS, ISSN: 1504-9795