Information about the scientific activities of our research & development center.
Meet our scientific board members
Michal Chudy, CTO and Co-founder of PowereX, holds a PhD in technical physics from the Vienna University of Technology and is a life member of Clare Hall, Cambridge. With a background in nuclear energy and superconductors, he founded Virtual PowerLabs in 2018. Michal has authored numerous scientific publications and currently leads PowereX’s research center, bridging the gap between innovative energy research and entrepreneurship.
Dr. Michal Chudy
Head of Research
Jonathan is a specialist in artificial intelligence (AI). Focuses on nature - inspired techniques (evolutionary calculations, optimization and neural networks) for problem solving in mathematics, robotics and energy. Jonathan also has experience in multimodal optimization as well as behavioral evolution in robotics. His current research interests include deeper application of AI in the energy industry .
Dr. Jonathan Mwaura
AI Scientist
Jonathan is a specialist in artificial intelligence (AI). Focuses on nature - inspired techniques (evolutionary calculations, optimization and neural networks) for problem solving in mathematics, robotics and energy. Jonathan also has experience in multimodal optimization as well as behavioral evolution in robotics. His current research interests include deeper application of AI in the energy industry .
Dr. Jonathan Mwaura
AI Scientist
Jakub Ševcech has experience in data science, machine learning, and software development, Jakub has held several key roles at Swiss Re, including Head of Scalable Analytics Solutions and Senior Data Scientist. He has also contributed to research at the Kempelen Institute of Intelligent Technologies and the Slovak University of Technology (FIIT STU), where his focus was on data analysis and stream data processing.
Dr. Jakub Ševcech
Data Scientist
Associate Professor at the University of Zagreb, is a specialist in renewable energy integration, energy storage, and power system optimization. He completed his PhD at the University of Zagreb, with research focused on bilevel optimization for maintenance scheduling, and conducted further research at the University of Castilla-La Mancha.
Dr. Hrvoje Pandžić
Associate Professor, University of Zagreb
Research papers and publications
We collaborate with communities to conserve the natural resources
Research report: Intelligent control of the small hydropower plant Trenčianske Biskupice 2
This paper describes the main results from the experimental intelligent control of a small hydropower plant.
Optimized Power Flows in Microgrid with and without Distributed Energy Storage Systems
This study presents a combined algebraic and power flow model of a microgrid. The aim of this study is to introduce a strong tool which is capable to compare physical parameters of a microgrid which are hardly possible to calculate only by common algebraic optimization methods.
Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting
This paper presents a new interpretable approach for multiple data streams clustering in a smart grid used for the improvement of forecasting accuracy of aggregated electricity consumption and grid analysis named ClipStream.
Empirical analysis of end-to-end QoS performance in urban cellular vehicular scenarios
In the context of the growing demand for ultra-reliable and low-latency communications (URLLC) in Vehicle-to-Everything (V2X) applications and edge computing offloading, there is a growing imperative to understand the end-to-end Quality of Service (QoS) performance in real-world cellular networks.
Edge to Cloud Task Offloading Optimization in Internet of Vehicles Networks
The Internet of Vehicles (IoV) refers to the growing number of connected vehicles, requiring efficient task scheduling and computation platforms. Our research focuses on communication types and quality of service improvements, particularly in Vehicle-to-Everything (V2X) communication.
Research report: Intelligent control of the small hydropower plant Trenčianske Biskupice 2
This paper describes the main results from the experimental intelligent control of a small hydropower plant.
Optimized Power Flows in Microgrid with and without Distributed Energy Storage Systems
This study presents a combined algebraic and power flow model of a microgrid. The aim of this study is to introduce a strong tool which is capable to compare physical parameters of a microgrid which are hardly possible to calculate only by common algebraic optimization methods.
Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting
This paper presents a new interpretable approach for multiple data streams clustering in a smart grid used for the improvement of forecasting accuracy of aggregated electricity consumption and grid analysis named ClipStream.
Empirical analysis of end-to-end QoS performance in urban cellular vehicular scenarios
In the context of the growing demand for ultra-reliable and low-latency communications (URLLC) in Vehicle-to-Everything (V2X) applications and edge computing offloading, there is a growing imperative to understand the end-to-end Quality of Service (QoS) performance in real-world cellular networks.
Edge to Cloud Task Offloading Optimization in Internet of Vehicles Networks
The Internet of Vehicles (IoV) refers to the growing number of connected vehicles, requiring efficient task scheduling and computation platforms. Our research focuses on communication types and quality of service improvements, particularly in Vehicle-to-Everything (V2X) communication.
Research report: Intelligent control of the small hydropower plant Trenčianske Biskupice 2
This paper describes the main results from the experimental intelligent control of a small hydropower plant.
Optimized Power Flows in Microgrid with and without Distributed Energy Storage Systems
This study presents a combined algebraic and power flow model of a microgrid. The aim of this study is to introduce a strong tool which is capable to compare physical parameters of a microgrid which are hardly possible to calculate only by common algebraic optimization methods.
Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting
This paper presents a new interpretable approach for multiple data streams clustering in a smart grid used for the improvement of forecasting accuracy of aggregated electricity consumption and grid analysis named ClipStream.
Empirical analysis of end-to-end QoS performance in urban cellular vehicular scenarios
In the context of the growing demand for ultra-reliable and low-latency communications (URLLC) in Vehicle-to-Everything (V2X) applications and edge computing offloading, there is a growing imperative to understand the end-to-end Quality of Service (QoS) performance in real-world cellular networks.
Edge to Cloud Task Offloading Optimization in Internet of Vehicles Networks
The Internet of Vehicles (IoV) refers to the growing number of connected vehicles, requiring efficient task scheduling and computation platforms. Our research focuses on communication types and quality of service improvements, particularly in Vehicle-to-Everything (V2X) communication.
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