Publications related to Co-optimising Design and Operation of Energy Systems using Reinforcement Learning

Related publications (17)

Load-shifting for cost, carbon, and grid benefits: A model-driven adaptive survey with German and Swiss households

Faten Ghali, Claudia Rebeca Binder Signer, Matteo Barsanti

Survey data helps understand user energy behaviour and inform policies supporting the transition to a renewable, user-centric electricity grid. To explore user responses to dynamic, hypothetical energy scenarios – such as time-varying electricity tariffs o ...

2025

On the Guarantees of Minimizing Regret in Receding Horizon

Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

Andrea Cavallaro

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

2025

How data structures affect generalization in Kernel Methods and Deep Learning

Umberto Maria Tomasini

Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

EPFL2025

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Federico Grasselli, Kevin Kazuki Huguenin-Dumittan, Sanggyu Chong, Wei Bin How

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

2025

Modeling, design and economics of PEC devices

Alexandre Dominique M. Cattry

Photo-electrochemical (PEC) devices allow for converting solar energy into chemical energy and for the production of energetically dense solar fuels. Light absorption, charge separation and transport, electrochemical reactions, and ionic transport are requ ...

EPFL2024

Critical analysis of decision variables for high-throughput experimentation (HTE) with perovskite solar cells

Mohammad Khaja Nazeeruddin, François Maréchal, Naveen Bhati

Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, ban ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Modelling of multi-energy systems of residential buildings with Calliope and validation of results

Ueli Schilt

Given the current and future importance of energy availability in the residential sector, there is great interest in reducing and optimising the energy consumption of single and multi-family houses. An energy system consisting of solar PV, heat pump, and b ...

IOP Publishing2023

Computational Conceptual Design – Typological Exploration Of Spatial Truss Systems Through Optimization

Corentin Jean Dominique Fivet, Jonas Warmuth

Exploring a wide range of relevant design options from the outset is crucial for every sound conceptual design process. Optimization techniques are generally employed to generate well-performing structural design options. However, focusing only on performa ...

2023

Multi-agent Learning with Privacy Guarantees

Elsa Rizk

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

EPFL2023

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

Martin Josifoski

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume ...

2023

Design and testing of a low-energy and -carbon prototype structure that adapts to loading through shape morphing

Ian Smith, Gennaro Senatore, Arka Prabhata Reksowardojo

This paper presents an experimental validation of a new design method for adaptive structures that counteract the effect of loading through shape morphing. The structure is designed to morph into target shapes that are optimal to take external loads throug ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Uncooperative Rendezvous in Space: Design of an Electronic Architecture for High Performance Avionic with Multi Sensor Input and Intensive Data Rate

Michaël Yannick Juillard

Active Debris Removal missions consist of sending a satellite in space and removing one or more debris from their current orbit. A key challenge is to obtain information about the uncooperative target. By gathering the velocity, position, and rotation of t ...

EPFL2022

Proof of the Contiguity Conjecture and Lognormal Limit for the Symmetric Perceptron

Emmanuel Abbé

YY We consider the symmetric binary perceptron model, a simple model of neural networks that has gathered significant attention in the statistical physics, information theory and probability theory communities, with recent connections made to the performan ...

IEEE COMPUTER SOC2022

Graph Representation Learning with Optimal Transport: Analysis and Applications

Effrosyni Simou

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

Capturing trade-offs between daily scheduling choices

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

ELSEVIER SCI LTD2022

Discretization Uncertainties of Flow and Fatigue Damage Simulations of a Reversible Francis Pump-Turbine at Off-Design Operation in Turbine Mode

Drazen Dujic, Daniel Biner, Philippe Alexandre Bontemps

In the framework of the XFLEX HYDRO H2020 European Project, a 5 MW reversible Francis pump-turbine prototype, equipped with an industrial full size frequency converter, is investigated to demonstrate enhanced grid regulation capacities of variable speed hy ...

SPRINGER INTERNATIONAL PUBLISHING AG2022