Publications by Loris Di Natale

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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 ...

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 ...

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 ...

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 ...

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 ...

Loris Di Natale

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

SIMBa: System Identification Methods Leveraging Backpropagation

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

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

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

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

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

Physically Consistent Neural ODEs for Learning Multi-Physics Systems