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View all news →- Announcement
The group's new website is live
We've launched this website as the group's home online — news, publications and projects will be posted here going forward.
Event[Sample] Group organizes hands-on workshop on satellite irradiance data
Placeholder summary: a hands-on workshop reportedly covered working with satellite-derived irradiance datasets.
Announcement[Sample] PhD defense scheduled for next semester
Placeholder summary: a PhD defense on renewable energy systems is reportedly scheduled for next semester.
Other[Sample] Numerical weather model integrated into forecasting chain
Placeholder summary: a numerical weather prediction model was reportedly integrated into the group's clear-sky forecasting chain.
- Grant
Lendület Day 2025 at the Hungarian Academy of Sciences
MTA marked Lendület Day by introducing the programme's 21 newly funded research groups and awarding the first Lendület Prize.
Introduction
The aim of the MTA-BME Momentum Renewable Energy Systems Research Group is to support the transition to renewable energy sources through research, and to see its results put to practical use. The group is based at the Department of Energy Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, supported by the Lendület (Momentum) Programme of the Hungarian Academy of Sciences.
The output of the two most widespread renewable technologies, photovoltaics and wind turbines, depends on the weather: it cannot be known exactly in advance, and it does not follow when electricity is actually needed. These two properties, uncertainty and intermittency, are today the main obstacles to integrating renewables into the power grid. They create additional costs on the energy market, tie up reserve capacity, and limit the renewable capacity that can be installed economically. As weather-dependent generation expands, the timing of production and demand becomes a central question in every field of energy engineering. Our group works on solutions to both challenges.
To reduce uncertainty, we develop forecasting methods that draw on ground measurements, weather forecasts and satellite imagery at the same time. Combining physical modelling with machine learning, we produce probabilistic forecasts that give not only the expected output but also the deviation to be expected from it. To match generation and demand in time, we study battery and hydrogen-based energy storage: together with solar and wind plants, these form hybrid renewable energy systems. To size such systems and optimise their operation, we build detailed technology models in which costs and environmental impacts carry as much weight as technical performance. We test our methods on real data from operating power plants, so that they hold up in practice.
We believe that renewable sources can form a reliable, economical and environmentally sound basis for electricity supply, and that our work can contribute to keeping the Earth a livable place for the generations to come.
Members

Dr. Martin János Mayer
Principal Investigator, Associate Professor

Dr. Axel Groniewsky
Researcher, Associate Professor

Dr. Viktória Barbara Kovács
Researcher, Assistant Professor

Dr. Dávid Csemány
Researcher, Assistant Professor

Dávid Markovics
PhD Student

Bence Sziffer
PhD Student

Emese Lévai
PhD Student

Gergő Bendegúz Békési
PhD Student

Dávid Dobák
PhD Student





