---
title: Illuminating the Global Energy Landscape - Leveraging AI and LLMs for Country-Specific Energy Modeling
categories:
  - Master Thesis
  - Artificial Intelligence
  - Machine Learning
  - Energy Systems Modeling
  - Data Science
  - Global Energy Transition
  - Climate Change Mitigation
  - Sustainable Energy
  - Interdisciplinary Research
render: false
---

### What?

Master Thesis Project

### Where?

IPESE Lab, EPFL Valais Campus (Sion), with possibilities for partial remote work. High-frequency exchanges within a dynamic research community.

### When?

Flexible start and end dates according to personal preference—mutually agreeable scheduling.

### Context

> “The future belongs to those who empower it.”

Are you ready to change the world by revolutionizing how we understand and plan for global energy transitions? The pressing challenge of climate change demands bold action, innovative tools, and fresh perspectives.

While tools like EnergyScope have enabled policymakers and researchers to explore future energy scenarios in select countries, the global stage remains uneven. Many regions, especially underrepresented ones, lack accurate, dynamic models to guide their sustainable energy transitions. This gap leaves billions in the dark—both figuratively and literally.

#### Here’s where you come in.

We’re seeking a trailblazer to harness the power of Artificial Intelligence (AI) and Machine Learning (ML), including cutting-edge Deep Learning and Large Language Models (LLMs), to transform static, outdated energy data into dynamic, country-specific datasets for every nation.

#### Your mission is to:

  - Democratize Energy Modeling: Build a comprehensive, AI-enhanced dataset that brings every country into the conversation, not just the usual suspects.
  - Illuminate Underrepresented Regions: Shine a light on areas often overlooked, providing them with the tools to plan their sustainable futures.
  - Drive Global Impact: Enable stakeholders worldwide to design effective decarbonization strategies, optimize energy systems, and enhance climate resilience.

This is more than a project—it’s a global movement toward equitable, data-driven decision-making in the fight against climate change.

#### Your profile

  - Visionaries with a strong understanding of energy system modeling.
  - AI Aficionados with experience or keen interest in coding (Python) and working with AI, ML, and LLMs.
  - Data Wizards skilled in data collection, preprocessing, and analysis.
  - Innovators familiar with machine learning techniques like deep learning, NLP, and reinforcement learning.
  - Interdisciplinary Thinkers ready to integrate technical, environmental, and socio-economic insights.

### Overview

The global challenge of transitioning to sustainable energy systems, aligned with climate change mitigation and adaptation goals, necessitates robust decision-support tools. Energy system models like EnergyScope enable policymakers, researchers, and stakeholders to explore future scenarios and evaluate the impacts of energy transition strategies.

While EnergyScope has been adapted to various countries (Switzerland, Belgium, France, Germany, Italy, Canada, China, etc.), it lacks a comprehensive, generic country-specific database for all nations. This limitation hinders the ability to capture evolving dependencies and dynamics in energy demand, renewable potential, and socio-economic behaviors—especially in underrepresented regions where data is sparse or outdated.

This project aims to address this critical gap by leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques, including Deep Learning and Large Language Models (LLMs). The goal is to transform static energy data into dynamic, region-specific datasets that capture uncertainties and evolving conditions, thereby enhancing existing energy system models.

By focusing on critical areas such as energy demand, renewable energy potential, socio-economic behaviors, and climate impacts, the project will:

  - Develop a globally comprehensive, AI-enhanced dataset for energy system modeling.
  - Address gaps in existing energy models, particularly in underrepresented regions.
  - Promote data-driven decision-making for sustainable energy transitions aligned with the Paris Agreement.

The deliverables will include over 100 country-specific datasets, open-source AI-driven tools, and resources that enable stakeholders to design effective decarbonization strategies, optimize energy systems, and enhance climate resilience.

### Skills and Background

  - Strong understanding of energy system modeling (e.g., courses like Modeling and Optimization of Energy Systems).
  - Knowledge of energy conversion processes.
  - Proficiency or keen interest in coding (Python preferred).
  - Interest and knowledge in AI and Large Language Models (LLMs).
  - Data science skills, including data collection, preprocessing, and analysis.
  - Familiarity with machine learning techniques such as deep learning, natural language processing (NLP), and reinforcement learning.
  - Interdisciplinary mindset to integrate technical, environmental, and socio-economic aspects.

### Objectives

  - Data Collection and Preprocessing:
  - Collect and preprocess energy, climate, and socio-economic data from various international and regional sources (e.g., IEA, OECD).
  - Clean and format data to ensure consistency and compatibility across different regions.
  - Address missing or incomplete data using AI-driven imputation methods.
  - AI Model Development and Data Integration:
  - Develop AI models, including deep learning algorithms for data enhancement.
  - Utilize NLP for extracting policy-related information from unstructured sources.
  - Apply reinforcement learning for scenario analysis and modeling dynamic relationships.
  - Integrate data sources to create a preliminary, regionally detailed dataset.
  - Dataset Validation and Open-Source Contribution:
  - Calibrate and validate the dataset against real-world benchmarks to ensure accuracy and reliability.
  - Use statistical methods, including Monte Carlo simulations, to assess uncertainties and variabilities.
  - Develop and release open-source AI-driven tools and algorithms for data integration and scenario modeling.
  - Global Energy Modeling and Simulation:
  - Integrate the dynamic datasets into EnergyScope to explore region-specific pathways toward climate resilience and decarbonization.
  - Analyze energy demand, renewable energy potential, climate-induced changes, and socio-economic behaviors under different climate trajectories.
  - Support Climate Change Mitigation and Adaptation:
  - Provide high-resolution insights into renewable energy potential and socio-economic adaptation to climate policies.
  - Contribute critical information to support climate change mitigation and adaptation strategies aligned with the goals of the Paris Agreement.

### Contacts
  - [Jonas Schnidrig](jonas.schnidrig@epfl.ch)
  - [Arthur Chuat](arthur.chuat@epfl.ch)

### Additional Information

For further reading and context, consider exploring the following references:

  - https://doi.org/10.5075/epfl-thesis-11101
  - https://www.mdpi.com/1996-1073/17/7/1718#
  - https://doi.org/10.3389/fenrg.2023.1164813
  - https://doi.org/10.3389/fenrg.2024.1433921
  - https://doi.org/10.1016/j.jenvman.2024.122537

Join us in illuminating the global energy landscape using AI and LLMs. Your contribution will be pivotal in creating comprehensive, dynamic datasets that empower accurate and inclusive energy system modeling worldwide, driving the transition toward a sustainable and renewable energy future.