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Kristina Orehounig

Dr.
Kristina Orehounig
PI
Alumni
Empa
kristina.orehounig@empa.ch
Curriculum Vitae
This NCCR will help to develop automation and control strategies to successfully transform our energy system to a more sustainable one.

Kristina Orehounig is head of the Laboratory of Urban Energy Systems at Empa and co-leading the group of Multi-Energy Systems within the lab. She is also lecturing at the department of Architecture at ETH Zurich, Switzerland. She received her PhD degree from the Technical University of Vienna, Austria. Her research interests include the development of sustainable concepts in building design and operation, the integration of renewable energy systems, and the simulation and optimization of buildings and urban energy systems. 

Scientific Publications

Published
Uncertainty-Aware Energy Flexibility Quantification of a Residential Building
2023 ISGT Europe
Pages 1-6
Julie Rousseau, Hanmin Cai, Philipp Heer, Kristina Orehounig, Gabriela Hug
Collection

Research projects

Title
Principal Investigators

Probabilistic prosumer side flexibilities for multi use-case applications

Gabriela Hug, Kristina Orehounig, Philipp Heer

Summary

The electric energy demand of buildings is increasing due to the electrification of the transportation and heating systems. While this puts an additional strain on the power grid, EVs and heat pumps (among others) are flexible loads that can be leveraged to keep the balance between generation and load. Coincidently, smart meters and edge computing resources (i.e. building automation systems) are deployed providing the means to access this flexibility. Hence, in this project, we will leverage building data to develop detailed models for the quantification and usage of flexibility at the building level for different time scales.

People involved

Philipp Heer
Gabriela Hug, Kristina Orehounig, Philipp Heer
Probabilistic prosumer side flexibilities for multi use-case applications
Gabriela Hug, Kristina Orehounig, Philipp Heer

The electric energy demand of buildings is increasing due to the electrification of the transportation and heating systems. While this puts an additional strain on the power grid, EVs and heat pumps (among others) are flexible loads that can be leveraged to keep the balance between generation and load. Coincidently, smart meters and edge computing resources (i.e. building automation systems) are deployed providing the means to access this flexibility. Hence, in this project, we will leverage building data to develop detailed models for the quantification and usage of flexibility at the building level for different time scales.

Philipp Heer
Gabriela Hug, Kristina Orehounig, Philipp Heer
190
c0bfb8d4-028d-43f0-ac62-762f1c3631a8

Deep Reinforcement Learning for Building Control: Physics-inspired Methods

Colin Jones, Philipp Heer, Kristina Orehounig

Summary

Buildings consume 30% and 40% of the end-use energy in Europe and worldwide respectively, but the increasing amount of sensors installed in new or retrofitted buildings gives rise to new data-driven control opportunities. Most controllers in existing buildings are however still rule-based, due to the required engineering to develop more advanced methods, such as Model Predictive Control (MPC). On the other hand, Reinforcement Learning (RL) has received increasing attention in the past years as a decision making paradigm that bypasses the need for models. Instead, an agent learns to take optimal decisions solely by interacting with the environment and getting rewarded/penalized for it.

We conjecture that incorporating prior physics-based knowledge in the model architectures or learning procedures will be a stepping stone towards generally applicable - i.e. data-efficient and reliable - DRL approaches. In this work, we will thus explore various solutions to introduce prior knowledge in models and DRL agents, design algorithms to apply them to buildings, and deploy the resulting controllers on case studies to assess their advantages in practice, primarily focusing on the building energy systems installed at the Empa NEST demonstrator.

People involved

Colin Jones
Philipp Heer
Colin Jones, Philipp Heer, Kristina Orehounig
Deep Reinforcement Learning for Building Control: Physics-inspired Methods
Colin Jones, Philipp Heer, Kristina Orehounig

Buildings consume 30% and 40% of the end-use energy in Europe and worldwide respectively, but the increasing amount of sensors installed in new or retrofitted buildings gives rise to new data-driven control opportunities. Most controllers in existing buildings are however still rule-based, due to the required engineering to develop more advanced methods, such as Model Predictive Control (MPC). On the other hand, Reinforcement Learning (RL) has received increasing attention in the past years as a decision making paradigm that bypasses the need for models. Instead, an agent learns to take optimal decisions solely by interacting with the environment and getting rewarded/penalized for it.

We conjecture that incorporating prior physics-based knowledge in the model architectures or learning procedures will be a stepping stone towards generally applicable - i.e. data-efficient and reliable - DRL approaches. In this work, we will thus explore various solutions to introduce prior knowledge in models and DRL agents, design algorithms to apply them to buildings, and deploy the resulting controllers on case studies to assess their advantages in practice, primarily focusing on the building energy systems installed at the Empa NEST demonstrator.

Colin Jones
Philipp Heer
Colin Jones, Philipp Heer, Kristina Orehounig
189
077ed802-beee-4804-94de-2b0e3109b69d

Decentralized control of multi-energy systems

Kristina Orehounig, John Lygeros

Summary

Electrical, thermal and chemical processes follow individual demand and supply patterns under distinct time scales, from sub-second (electrical) to weekly (thermal) and seasonal (chemical) applications. In these applications, digitalization enables a coordination of the related technologies. So, next to sector specific technological limitations, the systemic coupling of energy carriers needs to take into account  the coupling of different time scales as well as different production and consumption patterns. As the technological landscape is getting more decentral in terms of energy production, also more stakeholders are involved in the energetic supply chain. These actors can benefit if they share information on their capabilities and intended production and consumption. Moreover, information sharing and local decision making could potentially reduce the need for extending large-scale infrastructure, such as transmission grids or international imports of energy. The project investigates the use of distributed decision making and control methods to address the large scale nature of the problem, as well as data driven methods to deal with the uncertainty inherent in the problem. Validation of the methodological approach is foreseen on the NEST building at Empa.

People involved

Varsha N. Behrunani
Philipp Heer
Kristina Orehounig, John Lygeros, Varsha N. Behrunani, , Philipp Heer
Decentralized control of multi-energy systems
Kristina Orehounig, John Lygeros

Electrical, thermal and chemical processes follow individual demand and supply patterns under distinct time scales, from sub-second (electrical) to weekly (thermal) and seasonal (chemical) applications. In these applications, digitalization enables a coordination of the related technologies. So, next to sector specific technological limitations, the systemic coupling of energy carriers needs to take into account  the coupling of different time scales as well as different production and consumption patterns. As the technological landscape is getting more decentral in terms of energy production, also more stakeholders are involved in the energetic supply chain. These actors can benefit if they share information on their capabilities and intended production and consumption. Moreover, information sharing and local decision making could potentially reduce the need for extending large-scale infrastructure, such as transmission grids or international imports of energy. The project investigates the use of distributed decision making and control methods to address the large scale nature of the problem, as well as data driven methods to deal with the uncertainty inherent in the problem. Validation of the methodological approach is foreseen on the NEST building at Empa.

Varsha N. Behrunani
Philipp Heer
Kristina Orehounig, John Lygeros, Varsha N. Behrunani, , Philipp Heer
113
34ab7ee3-eae2-4be6-a009-595eb7b858b4