Organisation/Company: Mines Paris - PSL, Centre PERSEE & RTE
Research Field: Engineering Technology & Energy Technology
Position: Recognised Researcher (R2), Leading Researcher (R4), First Stage Researcher (R1), Established Researcher (R3)
Country: France
Application Deadline: 9 Jun 2025 - 22:00 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Offer Starting Date: 1 Oct 2025
Funding: Not funded through the EU Research Framework Programme
Research Infrastructure Staff Position: No
Title: "Advanced AI-based methods to exploit massive data for improved predictability of renewable energy generation."
Context and background:
Short-term energy forecasting for the next minutes to days ahead is crucial for the safe and economical operation of modern power systems and electricity markets, especially with high renewable energy sources (RES) penetration. This PhD project addresses RES forecasting at local, regional, and national levels, aiming to improve accuracy to reduce costs and enhance grid stability.
The project focuses on leveraging geographically distributed RES data, handling large and sometimes incomplete datasets, and ensuring forecast consistency across different scales. The French Transmission System Operator, RTE, relies on such forecasts for balancing and grid management, where even minor errors can lead to significant financial losses.
Scientific objectives:
The goal is to enhance RES production and net load predictability, especially under challenging conditions like extreme weather, data issues, and non-weather factors, using AI methodologies such as foundational models and deep learning. The project seeks to develop adaptable, large-scale, and multi-scale forecasting models.
Methodology and expected results:
The research will analyze current state-of-the-art techniques, develop AI-assisted filtering and classification tools, and incorporate additional data sources to improve forecasts. Benchmarking against existing models will identify areas for enhancement, leading to the development of resilient, accurate, and context-aware AI-based forecasting solutions.
Funding category: Cifre
PhD Title: Doctorat en Énergétique et Procédés
PhD Country: France
Candidate Requirements:
Preferred competencies include applied mathematics, statistics, probabilities, data science, machine learning, AI, and energy forecasting. A good level of French is recommended.
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