activity
20202026
most citedMachine learning for total cloud cover prediction

35 citations · 65 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ML2026

Improving the sharpness in neural network-based parametric post-processing of ensemble forecasts

Ágnes Baran, Máté Mihalina

Statistical post-processing has proven to be an effective tool in improving ensemble forecast of different weather variables. Case studies show that post-processing can remedy the…

stat.AP2025★ 1 cited

Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods

Martin János Mayer, Ágnes Baran, Sebastian Lerch +3

Accurate and reliable forecasting of photovoltaic (PV) power generation is crucial for grid operations, electricity markets, and energy planning, as solar systems now contribute a…

stat.AP2023★ 1 cited

Parametric model for post-processing visibility ensemble forecasts

Ágnes Baran, Sándor Baran

Although by now the ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still might suffer from lack of calibration and…

stat.ML2022★ 9 cited

A two-step machine learning approach to statistical post-processing of weather forecasts for power generation

Ágnes Baran, Sándor Baran

By the end of 2021, the renewable energy share of the global electricity capacity reached 38.3% and the new installations are dominated by wind and solar energy, showing global inc…

stat.AP2021★ 8 cited

Calibration of wind speed ensemble forecasts for power generation

Sándor Baran, Ágnes Baran

In the last decades wind power became the second largest energy source in the EU covering 16% of its electricity demand. However, due to its volatility, accurate short range wind p…

stat.AP2020★ 11 cited

Statistical post-processing of heat index ensemble forecasts: is there a royal road?

Sándor Baran, Ágnes Baran, Florian Pappenberger +1

We investigate the effect of statistical post-processing on the probabilistic skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid) ensemble forecasts, both…

stat.ML2020★ 35 cited

Machine learning for total cloud cover prediction

Ágnes Baran, Sebastian Lerch, Mehrez El Ayari +1

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres is…