Research Group
Marek Lóderer
Research areas: machine learning, ensemble learning, metaheuristic and nature inspired computation, time series forecasting
Position: Researcher
Marek applies machine learning, ensemble learning, and metaheuristic optimization algorithms to solve a variety of microgrid-related problems, focusing on prediction, optimization, and anomaly detection in energy consumption.
He presented the results of his work at local and international conferences and published his findings in scientific journals and proceedings of professional organizations such as ACM and IEEE. He was a member of research teams in national and international research projects, such as Newton H2020 and ICERISE, and industry projects in cooperation with sféra, ATOS and Instarea.
During his PhD studies at the Slovak University of Technology in Bratislava, he led a student team developing a learning platform to revitalize geography teaching in elementary and high school using VR and AR technologies. In other projects, he and his students created a web-based application for modeling and simulating behavior in microgrids, as well as a high-speed GPU accelerated columnar database (qikkDB) suitable for big data.
In 2021, he obtained a Doctoral degree in Informatics from the Technical University of Košice.
Selected Projects
Other notable projects
International Centre of Excellence for Research of Intelligent and Secure Information-Communication Technologies and Systems – II. Stage
ITMS 313021W404. Partner: Atos IT Solutions and Services s.r.o. , Rozinajova, V. – principal investigator for FIIT STU until August 2020
International Centre of Excellence for Research of Intelligent and Secure Information-Communication Technologies and Systems
ITMS 26240120039. 2014-2015. Partner: Atos IT Solutions and Services s.r.o., Rozinajova, V. – principal investigator for FIIT STU
Knowledge-Based Approaches for Intelligent Analysis of Big Data
APVV-16-0213. 2017-2021. Partner: Technical University of Kosice (prof. Jan Paralic), Rozinajova, V. – principal investigator for FIIT STU until August 2020
NEWTON – Networked Labs for Training in Sciences and Technologies for Information and Communication
H2020 688503. 2016-2019. Coordinator: Dublin City University, Rozinajova, V. – principal investigator for FIIT STU
Optimization in Microgrid
Slovak University of Technology in Bratislava – Grant scheme to support excellent teams of young researchers. 2019-2021. Loderer, M. – principal investigator
Selected Student Supervising
Master
- Gemeľa Juraj – Detection of non-standard customer behavior in the power grid. Defended 2021
- Kubík Dávid – Optimization of power load consumption in microgrids. Defended 2020
- Vidiečanová Dominika – Optimization of power load consumption using high-capacity batteries. Defended 2020
- Cuper Matúš – Identification of abnormal behavior of customers in the power grid. Defended 2019
- Matula Marek – Prediction of electricity consumption by ensemble learning. Defended 2018
- Halaš Peter – Prediction of electricity consumption using biologically inspired algorithms. Defended 2017
Bachelor
- Bubán Michal – Biologically inspired algorithm in R language. Defended 2020
- Fano Adam – Biologically inspired algorithm in R programming language. Defended 2020
- Šoltés Pavol – Biologically inspired algorithm in R language. Defended 2019
- Cuper Matúš – Optimizing configuration parameters of prediction methods. Defended 2017
- Cvíčela Martin – Electric load forecast including external factors. Defended 2016