---
title: (sorry - already taken) ~~Prospective data for Québec’s energy transition~~
categories:
  - Master project
  - Montréal
  - CIRAIG
render: false
---
### What?
Master Thesis work

### Where?
In CIRAIG’s lab (Montréal, Canada) or remotely from IPESE lab (Sion, Switzerland)

### When?
From September 2024 to February 2025

### Context
The government of Québec has set ambitious greenhouse gas (GHG) reduction targets for 2030 and 2050. However, energy transition is a complex task that requires systematic tools to support decision-makers. Optimization models are very needed, due to their ability of assessing the penetration of new technologies in the energy market and identifying optimal transition pathways respecting GHG reduction objectives.
EnergyScope is an **optimization-based energy system model** that has recently been adapted to **Québec** context. EnergyScope is working with current data (2020) of Québec’s energy system. However, collecting, generating, and integrating prospective data in the model is critical to identify transition pathways towards net-zero emissions by 2050. In addition, as prospective data is subject to significant **uncertainty**, a **stochastic perspective** should be taken to improve the results robustness and reliability.
As a starting point, the prospective investment costs of the main low-carbon energy technologies in EnergyScope have been estimated using [learning curve theory](https://infoscience.epfl.ch/record/303793). However, the prospective database must be further enriched with additional economic, technical and energy demand and potential data. For that purpose, data may either be collected from the literature, or generated with **prediction models** based on **machine learning** or **learning curves** for example.

### Skilss and background
The master student should have a strong understanding of energy modelling, and the energy sector in general. Knowledge in other types of models such as machine learning is a plus.
In terms of skills, the student should have robust coding skills, to be able to build and assess prediction

### Objectives
- Build a prospective database, including economic, technical and demand energy data.
- Develop Québec-specific prediction models to challenge data from the literature or fill gaps, for instance energy demand data (power, heat, and mobility, in different sectors).
- Integrate results into EnergyScope model and identify optimal transition scenarios.

### Contacts
[Jonas Schnidrig](mailto:jonas.schnidrig@epfl.ch).
[Matthieu Souttre](mailto:matthieu.souttre@epfl.ch).
