Publications by Loris Di Natale

SIMBa: System Identification Methods Leveraging Backpropagation

Colin Neil Jones, Loris Di Natale, Giancarlo Ferrari Trecate, Muhammad Zakwan

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

2024

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Loris Di Natale

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

EPFL2024

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

Colin Neil Jones, Loris Di Natale, Giancarlo Ferrari Trecate, Bratislav Svetozarevic, Muhammad Zakwan

2023

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

Colin Neil Jones, Loris Di Natale, Yingzhao Lian, Emilio Maddalena, Jicheng Shi

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These ...

2023

Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models

Colin Neil Jones, Loris Di Natale, Bratislav Svetozarevic

2023

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Colin Neil Jones, Loris Di Natale, Bratislav Svetozarevic

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 policies. On the other hand, we postulate that expert knowledge of the syste ...

2023

Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments

Christian Baumann, Melanie Nicole Zeilinger, Loris Di Natale, Bratislav Svetozarevic

2022

Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies

Colin Neil Jones, Loris Di Natale, Bratislav Svetozarevic

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them. However, since weeks long experiments are needed to assess the performance of a building controller, people still have to rely on ...

2021

The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland

Loris Di Natale, Bratislav Svetozarevic

Energy systems are undergoing a profound transition worldwide, substituting nuclear and thermal power with intermittent renewable energy sources (RES), creating discrepancies between the production and consumption of electricity and increasing their depend ...

2021