Energo Group
Energo @ KInIT

The Energo group dedicates itself to the research of data analysis in power engineering domain. We investigate existing and novel approaches to solving tasks such as prediction of energy consumption or production, grid components optimization, anomaly detection and microgrid optimization.


We employ statistical, Artificial Intelligence and various ensemble methods.


In predictive modeling, our outcomes support optimal operation of power suppliers in energy market, they also facilitate the emulation of feasibility of a planned microgrid. Anomaly detection helps to identify unusual situations and allows timely reactions to problems in power grid. Microgrid optimization enables estimation of optimal parameters of its components and calculate the return of investments into smart technologies.

Research team

Viera Rozinajová
(Group leader)

Associate professor focusing on intelligent data analysis methods. She has led several projects which were focused on data analysis, software development and related research. She was 8 years a vice-dean of the faculty responsible for research, projects and industry cooperation at FIIT STU.

Mária Lucká

Full professor focusing on research of fast and effective algorithms and Big Data processing in various applications, such as bioinformatics, smart grid optimization. Her research concerns parallel and distributed computing, artificial intelligence and biologically inspired computing.

Anna Bou Ezzeddine

Associate professor focusing on nature inspired computing, optimization and data mining. Her research is oriented on energy production and microgrid optimization, creating an effective forecasting using nature inspired computing to predict macroeconomic development of the country.

Gabriela Grmanová

Assistant professor focusing on artificial intelligence, knowledge discovery and data mining. Her research is focused on energy production and consumption forecasting, microgrid optimization and flow cytometry data analysis.

Marek Lóderer

Researcher of prediction and optimization approaches in the power load domain with focus on ensemble prediction methods and metaheuristic optimization algorithms. For several years, he has been actively studying machine learning methods and their application in the field of energetics. He was part of a team that worked on visualization and optimization of microgrids.

Petra Vrablecová

Post-doc researcher focusing on data mining and high-volume data processing. Her research involves time series analysis, stream mining, and predictive modelling in power engineering domain (e.g., power demand and production forecasting).

Viera Rozinajová
(Group leader)

Associate professor focusing on intelligent data analysis methods. She has led several projects which were focused on data analysis, software development and related research. She was 8 years a vice-dean of the faculty responsible for research, projects and industry cooperation at FIIT STU.

Mária Lucká

Full professor focusing on research of fast and effective algorithms and Big Data processing in various applications, such as bioinformatics, smart grid optimization. Her research concerns parallel and distributed computing, artificial intelligence and biologically inspired computing.

Anna Bou Ezzeddine

Associate professor focusing on nature inspired computing, optimization and data mining. Her research is oriented on energy production and microgrid optimization, creating an effective forecasting using nature inspired computing to predict macroeconomic development of the country.

Gabriela Grmanová

Assistant professor focusing on artificial intelligence, knowledge discovery and data mining. Her research is focused on energy production and consumption forecasting, microgrid optimization and flow cytometry data analysis.

Marek Lóderer

Researcher of prediction and optimization approaches in the power load domain with focus on ensemble prediction methods and metaheuristic optimization algorithms. For several years, he has been actively studying machine learning methods and their application in the field of energetics. He was part of a team that worked on visualization and optimization of microgrids.

Petra Vrablecová

Post-doc researcher focusing on data mining and high-volume data processing. Her research involves time series analysis, stream mining, and predictive modelling in power engineering domain (e.g., power demand and production forecasting).

Viera Rozinajová
(Group leader)

Associate professor focusing on intelligent data analysis methods. She has led several projects which were focused on data analysis, software development and related research. She was 8 years a vice-dean of the faculty responsible for research, projects and industry cooperation at FIIT STU.

Mária Lucká

Full professor focusing on research of fast and effective algorithms and Big Data processing in various applications, such as bioinformatics, smart grid optimization. Her research concerns parallel and distributed computing, artificial intelligence and biologically inspired computing.

Anna Bou Ezzeddine

Associate professor focusing on nature inspired computing, optimization and data mining. Her research is oriented on energy production and microgrid optimization, creating an effective forecasting using nature inspired computing to predict macroeconomic development of the country.

Gabriela Grmanová

Assistant professor focusing on artificial intelligence, knowledge discovery and data mining. Her research is focused on energy production and consumption forecasting, microgrid optimization and flow cytometry data analysis.

Marek Lóderer

Researcher of prediction and optimization approaches in the power load domain with focus on ensemble prediction methods and metaheuristic optimization algorithms. For several years, he has been actively studying machine learning methods and their application in the field of energetics. He was part of a team that worked on visualization and optimization of microgrids.

Petra Vrablecová

Post-doc researcher focusing on data mining and high-volume data processing. Her research involves time series analysis, stream mining, and predictive modelling in power engineering domain (e.g., power demand and production forecasting).

Funding partners

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