La politisation de la Boucle du Hainaut compromet l’avenir de la province, du pays et du climat

Téléchargez la version pdf de l'article Auteur : Prof. Damien Ernst – Carte blanche publiée dans le journal l’Echo le 23-05-2023. Qui ne rêverait pas d’un Hainaut prospère où le taux d’emploi serait à des niveaux jamais atteints ?  Qui ne rêverait pas que la Belgique puisse bénéficier de l’énergie produite par ses éoliennes en [...]

24 mai 2023|

Job offers – Research positions in Machine Learning/Optimisation for Energy Systems

The research positions are about combining modelling, simulation, optimisation, and machine learning techniques in order to investigate several technical, economic and regulatory aspects induced by major upcoming changes in energy (and in particular, electricity) generation, transmission, distribution and consumption in the context of the Energy transition. In particular, we are looking to fill several positions [...]

9 mars 2023|

Recurrent neural networks, hidden states and beliefs in partially observable environments

Despite achieving impressive performances on various tasks, modern artificial intelligence (AI) systems have become complex black box models. A growing body of work aspires to open the box and understand its internal functioning. In this new article (Lambrechts et al., 2022), we follow this field of research by studying the internal representation that intelligent agents learn through reinforcement learning (RL), when those agents act in partially observable environments (POEs). In particular, the informational content of the memory of those agents is studied when the latter are trained to act optimally in maze and orientation tasks.

1 septembre 2022|

The Graph-Based Optimization Modeling Language (GBOML)

The decarbonisation of energy systems is one of the main challenges of our century. Finding the optimal technological mix to achieve that goal is a major concern for policy and decision makers alike. These problems are known as energy system planning and sizing and are often tackled by Mixed Integer Linear Programming (MILP). In order [...]

25 avril 2022|

Gym-ANM: Reinforcement learning environments for active network management tasks in electricity distribution systems

Download the paper Summary: Active network management (ANM) of electricity distribution networks include many complex stochastic sequential optimization problems. These problems need to be solved for integrating renewable energies and distributed storage into future electrical grids. In this work, we introduce Gym-ANM, a framework for designing reinforcement learning (RL) environments that model ANM tasks in [...]

14 mars 2021|

Remote Renewable Hubs for Carbon-Neutral Fuel Production

Decarbonising sectors such as aviation, heating and industry has proved difficult via direct electrification. Hence, the synthesis of carbon-neutral fuels and feedstocks from renewable electricity has received much attention in recent years. However, in European countries such as Belgium, the theoretical amount of renewable electricity that may be produced is known to be insufficient to [...]

24 février 2021|

Sector coupling for the energy transition: the case of the Dutch energy system

The topic of sector coupling and the role it may play in the energy transition has recently received a great deal of attention in European energy policy circles. In particular, power-to-gas technologies, which enable the production of hydrogen via water electrolysis as well as the synthesis of methane via the hydrogenation of carbon dioxide (the [...]

4 juillet 2020|

An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

Download the research paper Retailers and major consumers of electricity generally purchase a critical percentage of their estimated electricity needs years ahead on the forward markets. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. This decision-making problem [...]

11 juin 2020|

An Application of Deep Reinforcement Learning to Algorithmic Trading

Download the research paper This research paper presents a novel deep reinforcement learning (DRL) solution to the decision-making problem behind algorithmic trading in the stock markets: selecting the appropriate trading action (buy, hold or sell shares) without human intervention. Naturally, the core objective is to achieve an appreciable profit while efficiently mitigating the trading risk. [...]

15 avril 2020|

A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding

Download the research paper This paper addresses a key issue in the context of the Energy Transition: the computation of trading strategies for valorizing storage units in markets with large renewable energy integration. In particular, we focus on (short-term) intra-day markets and propose a reinforcement learning-based approach for computing well-performing strategies. The first part of [...]

13 avril 2020|