Seamless probabilistic weather forecasts for renewable energy prediction
Ivana Aleksovska is a post-doctoral researcher at the CNRM, the National Centre for Meteorological Research. Her research focuses on uncertainty quantification of weather forecasts.
The renewable energy sector is highly dependent on weather conditions and the reliability of weather forecasts has a direct impact on energy production. Weather forecasts are now a major component of various energy production estimation systems which, as a decision support tool, help energy suppliers to make optimal decisions in order to avoid potential losses in the market.
Regarding weather forecasts, SMART4RES concentrates on the development of next-generation weather forecasting solutions, with an optimal use of varied and distributed sources of data, and a proper representation of forecast uncertainty. However, the proper consideration of weather uncertainty in RES forecasting remains a challenge.
Schematic representation of the seamless matching between the Arome and Arpege ensemble forecasts.
The aim is to develop an algorithm that find the best association between Arome and Arpège forecasts
Credit: Météo-France
Within Smart4RES, Ivana uses two global and regional ensemble prediction systems (EPS), covering different spatio-temporal scales, to provide seamless weather forecasts of the wind speed required in wind turbine energy production models. Ivana applies a novel approach to design seamless ensemble forecasts from the combination of the two EPSs, Arome-EPS and ARPEGE-EPS. This approach relies on a former project, where Ivana developed a seamless ensemble forecast combining three EPSs, for temperature forecasting in agricultural application for crop protection treatment reduction. The proposed method takes advantage of the increased performance of high-resolution EPS for short lead times, while ensuring a smooth transition to larger-scale EPS for longer lead times.
More about Ivana Aleksovska
On her background
Ivana graduated in Applied Mathematics (University Ss .Cyril and Methodius, Skopje, Macedonia) in 2017. In 2020, she defended her thesis ‘Improve short- and medium-term predictions of agronomic models by better taking into account the uncertainty of weather forecasts’ at the University Toulouse III – Paul Sabatier, France.
On her research interests
Uncertainty quantification, Black box modelling, Ensemble weather forecasting, Seamless forecast, Renewable energy modelling
On choosing Smart4RES and Météo-France for her research
” At Météo-France, particularly in its research department, the CNRM (Centre national de recherches météorologiques), we can satisfy our curiosity in research and theoretical, experimental and instrumental studies in the field of atmospheric sciences, where weather forecasting and climate evolution are nourished by the reciprocal enrichment between research and operational application. Weather forecasting has unavoidable inputs in several other sectors, notably the renewable energy sector which is the future of our planet. This is of particular interest to me and is the reason why I am part of the SMART4RES project.”
Where she sees herself in 5 years
“Working on uncertainty quantification in meteorological service.”
On Mentors with an impact
“Laure Raynaud for transmitting to me her passion for weather forecasting, a new world for me so exciting.”