Dr. Bratislav Svetozarevic




Swiss Federal Laboratories for Materials Science and Technology

Überlandstrasse 129

CH-8600 Dübendorf



Office tel: + 41 58 765 65 35


Google Scholar, Research Gate




Dr. Bratislav Svetozarevic is a post-doctoral researcher at the Urban Energy Systems laboratory at Empa, working within the ehub group. He joined Empa in November 2018.

His current work is related to data-driven, self-optimizing control algorithms for processes and systems in buildings. The goal of self-optimizing control algorithms is to improve the operational efficiency of buildings and offer (close-to) optimal control performance over the lifespan of a building without a substantial or any need for human interventions. Therefore, besides energy savings, these algorithms reduce the need for maintenance in buildings and allow for continuous commissioning, i.e. automatic recommissioning after a part of the system was replaced, thus reducing the overall buildings' operational costs. Bratislav's particular interests lie in the transferability and large-scale applicability of these algorithms within different building systems and districts with integrated renewable energy sources, storage, and electric vehicles. 


Bratislav Svetozarevic has a highly interdisciplinary background and his research is bridging the fields of automatic control, machine learning, building systems, and energy management. Before moving to EMPA, he was part of several single- and inter-disciplinary teams in academia (Chair of Architecture and Building Systems, ETH Zurich  – dynamic photovoltaic building envelopes, 2014 - present, Automatic Control Laboratory, ETH Zurich – fault detection and isolation for wind turbines, 2010-2012, and ETF Robotics, University of Belgrade – control algorithms for compliant, tendon-driven robotic mechanisms, 2009-2010) and industry (spin-off Siltectra GmbH (2013), acquired in 2018 – R&D engineer for automated waste-free splitting of crystal materials). He has lead and co-authored publications in engineering (IEEE, Applied Energy, Energy and Buildings) and science (Nature) in the domain of energy management, smart building systems, and robotics. He is a leading co-author of a Nature Energy paper on dynamic BIPV facades, which was also featured on the cover page.


Bratislav Svetozarevic received a PhD at the Swiss Federal Institute of Technology (ETH) in Zurich. He worked at the Chair of Architecture and Building Systems under the supervision of Prof. Arno Schlueter. In his PhD work he developed a hybrid soft-hard-material actuator and applied it within a multi-element dynamic photovoltaic façade. He is a leading co-author of a patent that was awarded a prestigious Spark award for Top 20 innovations at ETH Zurich. 



Ongoing research projects


Data-driven, self-tuning controllers for large-scale deployment

More than 90% of industrial controllers are still based on proportional-integral-derivative (PID) and rule-based (RB) control algorithms, as no other algorithms match the simplicity, clear functionality, and applicability of these two types. However, the manual tuning of PID and RB controllers, which requires optimization of a few parameters, is a time-consuming task and depending on the controlled process, may require high-skilled personal. During the lifetime of a system, due to aging or changing of some components of the system, the overall process conditions typically change, which calls for manual re-tuning of the controllers. This is also referred to as re-commissioning and creates additional maintenance (personal costs) and operational costs, due to the downtime of the system during the re-tuning. 

More sophisticated controllers than PID and RB exist, such as ones based on model predictive control (MPC), but they typically require complex physics-based models for proper functioning. However, obtaining these models can be quite expensive, as they require a substantial time of highly-skilled personals and may differ among selves even within a single class of systems. One such example are buildings. Due to different designs, construction properties, orientation, climate zones, and occupants profiles of buildings, their dynamics differ from case to case. Therefore, an alternative control strategy that has a property of self-adapting is a preferable solution in building control. Due to the availability of sensors and databases, self-adaptivity can be realized as an algorithm that learns, in terms of machine learning, from the past measurements and automatically derives decisions that will influence (modify, adapt) the future control strategy. Such a constantly adapting control strategy can achieve close-to-optimal control performance over the lifespan of the controlled system, after the initial commissioning, can reduce maintenance, operational, and re-commissioning costs after upgrades of the system. Additional aspects of such a control strategy are also desirable, if not required, such as stability guarantees, safety guarantees, and robustness.

As buildings are one of the largest contributors to climate change, requiring about 40% of the final world energy for their operation and this is a growing trend due to urbanization, they come to the forefront of research on energy savings. Given also the global trend towards the integration of renewable energy resources into buildings and on-site energy generation, storage, and transformation capabilities, and given the rise in the number of electric vehicles (EVs), the energy management problem of a building becomes a challenging problem. Many of the phenomena there are of non-linear and stochastic nature, which complicate further the control problem.

In this project we aim to develop new data-driven self-adaptive control strategies and compare them to the state-of-the-art control strategies, as well as to the classical PID, RB, and MPC controllers. In this, we will particulary look at the scalability potentials of the algorithms, i.e. in the algorithms that do not require substantial, if any at all, engineering or expert skills, to apply them to another system within the same class of systems or even from another class of systems. Our current efforts involve the development of controllers for heat pump systems, room temperature control algorithms, and controllers for smart charging of bidirectional EVs when coupled to buildings and grids.  

Funding body: internal

Partners: ETH Zurich, EPFL, Industry



Ongoing student supervisions 

  • PhD student, Loris Di Natale, MSc EPFL, project title: Coupled EV smart charging and building energy management using data-driven learning-based methods
    Co-supervised with Prof. Colin Jones (main supervisor, EPFL) and Prof. Melanie Zeilinger (ETH Zurich)


2019, Autumn semester

  • MSc student, Christian Baumann, MSc ETH Zurich, project title: Joint control of bidirectional electric vehicle charging and home energy scheduling using reinforcement learning. Co-supervised with Prof. Melanie Zeilinger (ETH Zurich)


2019, Spring semester

  • Intern, Angelos MikelisMSc NTUA, project title: PID tuning using extremum seeking: Application to a heat pump system, Co-supervised with Prof. Miroslav Krstic (UC San Diego)
  • MSc student, Jeffrey Lungthok, MSc ETH Zurich, project title: Safe Bayesian optimization with improved convergence speed for online autotuning of PI controllers: Simulation and experimental results, Co-supervised with Prof. Andreas Krause (ETH Zurich)




Google ScholarResearch Gate

Journal articles (selected)

  • B. Svetozarevic, M. Begle, P. Jayathissa, Z. Nagy, S. Caranovic, R. Shepherd, I. Hischier, J. Hofer,  A. Schlueter, "Dynamic photovoltaic building envelopes for adaptive energy and comfort management", Nature Energy (4), 671-682, 2019. https://doi.org/10.1038/s41560-019-0424-0
  • B. Svetozarevic, M. Begle, S. Caranovic, Z. Nagy, A Schlueter, "Quick-cast: A method for fast and precise scalable production of fluid-driven elastomeric soft actuators", Extreme Mechanics Letters, in press. https://arxiv.org/abs/1810.05920
  • Z. Nagy, B. Svetozarevic, P. Jayathissa, M. Begle, J. Hofer, G. Lydon, A. Willmann, A. Schlueter, "The Adaptive Solar Facade: From concept to prototypes", Frontiers of Architectural Research 5 (2), 143-156, 2016. https://doi.org/10.1016/j.foar.2016.03.002

Conference papers (selected)

  • B. Svetozarevic, Z. Nagy, J. Hofer, D. Jacob, M. Begle, E. Chatzi, and A. Schlueter, "SoRo-Track: A Two-Axis Soft Robotic Platform for Solar Tracking and Building-Integrated Photovoltaic Applications", IEEE International Conference on Robotics and Automation (ICRA), 2016. https://doi.org/10.1109/ICRA.2016.7487700
  • B. Svetozarevic, Z. Nagy, D. Rossi, A. Schlueter, "Experimental Characterization of a 2-DOF Soft Robotic Platform for Architectural Applications", Proceedings of the Robotics: Science and Systems, Workshop on Advances on Soft Robotics, Berkeley, CA, USA, 2014. https://www.research-collection.ethz.ch/handle/20.500.11850/92114
  • B. Svetozarevic, P. M. Esfahani, M. Kamgarpour, J. Lygeros, "A Robust Fault Detection and Isolation Filter for a Horizontal Axis Variable Speed Wind Turbine", Proceedings of the American Control Conference, 4453 – 4458, 2013. https://doi.org/10.1109/ACC.2013.6580526


  • B. Svetozarevic, A. Schlueter, M. Begle, J. Prageeth, and S. Caranovic, Hybrid hard-soft-material pneumatic actuator with adjustable mechanical impedance, International Application No. PCT/EP2018/080425 (link)