105 citations · 265 across the 13 of their papers we have counts for
17 papers
Experimental Validation for Distributed Control of Energy Hubs
Varsha Behrunani, Philipp Heer, John Lygeros
As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-p…
Degradation-aware data-enabled predictive control of energy hubs
Varsha Behrunani, Marta Zagorowska, Mathias Hudoba de Badyn +3
Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy…
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs
Varsha Behrunani, Hanmin Cai, Philipp Heer +2
The increasing penetration of renewable energy resources has transformed the energy system from traditional hierarchical energy delivery paradigm to a distributed structure. Such d…
Stochastic MPC for energy hubs using data driven demand forecasting
Varsha Behrunani, Francesco Micheli, Jonas Mehr +2
Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub…
Designing Fairness in Autonomous Peer-to-peer Energy Trading
Varsha Behrunani, Andrew Irvine, Giuseppe Belgioioso +3
Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory. In this paper,…
Towards Scalable Physically Consistent Neural Networks: an Application to Data-driven Multi-zone Thermal Building Models
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
With more and more data being collected, data-driven modeling methods have been gaining in popularity in recent years. While physically sound, classical gray-box models are often c…
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic +3
Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, th…
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing…
Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement…
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer +1
Due to their high energy intensity, buildings play a major role in the current worldwide energy transition. Building models are ubiquitous since they are needed at each stage of th…
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
Felix Bünning, Benjamin Huber, Adrian Schalbetter +5
Because physics-based building models are difficult to obtain as each building is individual, there is an increasing interest in generating models suitable for building MPC directl…
Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision
Hanmin Cai, Philipp Heer
Active building energy management holds potential to reduce global energy-related emissions and support flexible operations of future low-carbon systems. This requires to integrate…
Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes
Nami Hekmat, Hanmin Cai, Thierry Zufferey +2
Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids. A promising means is to use the so-…
Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
B. Svetozarevic, C. Baumann, S. Muntwiler +3
This work presents a fully data-driven, black-box pipeline to obtain an optimal control policy for a multi-loop building control problem based on historical building and weather da…
Input Convex Neural Networks for Building MPC
Felix Bünning, Adrian Schalbetter, Ahmed Aboudonia +3
Model Predictive Control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for build…
Temporal Resolution of Measurements and the Effects on Calibrating Building Energy Models
Fazel Khayatian, Andrew Bollinger, Philipp Heer
With the recent interest in installing building energy management systems, the availability of data enables calibration of building energy models. This study compares calibration o…
Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps
Felix Bünning, Joseph Warrington, Philipp Heer +2
With the increased amount of volatile renewable energy sources connected to the electricity grid, and the phase-out of fossil fuel based power plants, there is an increased need fo…