PhD #3 at Mines Paris in Data Science & Energy: "Seamless forecasting of local energy production and demand using multiple heterogeneous data sources"

27 Jan 2024
Job Information

Organisation/Company

Mines Paris - PSL, Centre PERSEE

Research Field

Engineering
Technology » Energy technology
Mathematics

Researcher Profile

Recognised Researcher (R2)
Leading Researcher (R4)
First Stage Researcher (R1)
Established Researcher (R3)

Country

France

Application Deadline

28 Feb 2024 - 22:00 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Title:   "Seamless forecasting of local energy production and demand using multiple heterogeneous data sources"

Context and background:

Short-term energy forecasting for the next minutes to days ahead, is a prerequisite for the economic and safe operation of modern power systems and electricity markets especially under high renewable energy sources (RES) penetration. The different contexts of application make that end-users require models that have a broad number of properties especially when they are applied operationally. They should cover multiple time frames (from minutes to days ahead) and multiple RES technologies (i.e. wind, solar, hydro) as well as their aggregations (i.e. in the form of virtual power plants – VPP). They should use as input the very large amount of data available, while dealing efficiently with dimensionality. The data sources may be measurements from the power plants, various types of satellite images, sky camera images, various feeds of numerical weather prediction and others. They should be generic enough to be easily replicable to different sites or demand forecasting. They should also be resilient against imperfect or corrupted data streams; be interpretable enough; and be able to deal with structural changes in the physical system (e.g. addition of assets to a VPP or equipment in a smart home). So far separate models are developed for each of these aspects. The thesis is realized in the frame of the PEPR TASE project Fine4Cast coordinated by the supervisors of this thesis. PERSEE has an international visibility in the field of energy forecasting thanks to a long track of national and European projects, PhDs and publications in the area.

Scientific objectives:

This thesis will develop a seamless forecasting approach for net-load and joint load and renewable production that meets the above requirements, while being at least as accurate as the currently used partial models. It will also preserve privacy of the different data sources. The modelling approach should be probabilistic giving the possibility to estimate the uncertainty in the forecasts. Combination methods of probabilistic forecasts will be assessed.

 

Methodology and expected results:  

A seamless method has been proposed by PERSEE that optimally combines the available data sources to derive a probabilistic forecast of RES production at multiple temporal scales and aggregation levels. Adapting this seamless concept to local demand or net-load has not yet been proposed in the literature. The methodology will start by identification of adequate explanatory variables from multiple data sources (multiple weather predictions and simulations, local measurements, multiple types of satellite-based images, etc.). The second step will ensure the scalability of the forecasting approach to large dimensions and the adaptivity to structural change in the production and demand assets. Validation will be done using available real-world data sets. Emphasis will be given on assessing the contribution of each available data source in a cost-benefit analysis context.

Funding category: Autre financement public
Project PEPR TASE "Fine4Cast": "Next Generation Energy Demand and Renewable Production Forecasting Tools for Fine Geographical and Temporal Scales"
PHD title: Doctorat en Énergétique et Procédés
PHD Country: France

Requirements
Specific Requirements

PROFILE:

Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).

Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies:

applied mathematics, statistics and probabilities
data science, machine learning, artificial intelligence
energy forecasting
power system management, integration of renewables
optimization

 

Expected level in french : Good level 

Expected level in english : Proficiency

 

Desired starting date is 1st of March 2024 or on a mutually agreed date until the 1st of September 2024. Duration 36 months. Full-time position.

 

For more information please contact Prof. Georges Kariniotakis and Dr Simon Camal. 

 

 

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

Mines Paris - PSL, Centre PERSEE

Country

France

City

Sophia-Antipolis

Geofield

Where to apply

Website

https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/119712

Contact

Website

http://www.persee.mines-paristech.fr

STATUS: EXPIRED