Phd #4 at Mines Paris in Data Science - Sophia Antipolis, France - Mines Paris - PSL, Centre PERSEE

Mines Paris - PSL, Centre PERSEE

Entreprise vérifiée

Sophia Antipolis, France

il y a 3 semaines

Posté par:

Sophie Dupont

beBee Recruiter

Description

PhD #4 at Mines Paris in Data Science & Energy: "Flexibility-aware forecasting of local energy demand":

Réf

-
ABG-119787

Sujet de Thèse 29/01/2024
Autre financement public

Mines Paris

PSL, Centre PERSEE
Lieu de travail
Sophia-Antipolis
Provence-AlpesCôte d'Azur
France
Intitulé du sujet
PhD #4 at Mines Paris in Data Science & Energy: "Flexibilityaware forecasting of local energy demand"
Champs scientifiques
Sciences de l'ingénieur
Energie
Mathématiques

Mots clés

Energy forecasting, Demand forecasting, Smart Grid, Energy digitalisation, data science, Artificial intelligence, Smart grids, Energy transition

Description du sujet:

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

Context and background:

Short-term forecasts of energy demand (electricity, heating/cooling, gas) at local level, ranging from a single household up to a group of buildings, a district, a node of the grid or a microgrid, become more and more necessary in the context of smart grids.

Several new business models emerge, where the involved actors require such forecasts (together with information on the associated uncertainty) for a few minutes to days ahead in order to manage the corresponding energy systems.

The objective may be auto-consumption (when generation and/or storage capabilities are available), provision of flexibility services to the grid, energy exchanges with other members of an energy community a.o.

Although the literature on electricity forecasting at a national level is broad and the accuracy of existing models is very high, this is not the case for demand at local level.

The existing models are not adapted to the ongoing transformation and digitalization of energy networks and the electrification of new usages that results in increasing demand by consumers (i.e. electric vehicles charging). Furthermore, the integration of high shares of renewables (wind, solar) is a challenge for grid operators. It becomes more and more necessary to adopt solutions that permit to adapt consumption to the variable renewable generation. For that, they deploy technologies that enable more flexibility of the consumption (e.g. load shifting, EV charging in zones with lower grid operational constraints, etc.). The activation of such flexibility options becomes more frequent and concerns more and more consumers. This induces spatio-temporal modifications in energy consumption patterns.

As digital information has become central in the organization and dynamic behavior of both rural and urban territories, an efficient treatment of contextual information regarding the expected use of energy at the local scale is needed.

Scientific objectives:

The overall objective of the thesis is to develop a forecasting approach for local energy demand that is flexibilityaware, i.e. that can adapt to flexibility activations from energy networks (electricity, heat/cold, gas). The forecasting approach will be able to integrate contextual information relative to local situations, e.g. traffic, environmental conditions, weather conditions, news and social media.

Methodology and expected results:

Nature du financement:

Autre financement public

Précisions sur le financement:

Project PEPR TASE "Fine4Cast": "Next Generation Energy Demand and Renewable Production Forecasting Tools for Fine Geographical and Temporal Scales"
Présentation établissement et labo d'accueil:

Mines Paris

PSL, Centre PERSEE

The
PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems.

It is built around three structuring themes:
i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.

The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France.

Its workforce is around 50 people.

Site web:

Intitulé du doctorat:

Doctorat en Énergétique et Procédés
Pays d'obtention du doctorat:
France

Etablissement délivrant le doctorat:

Mines Paris

PSL (Ecole Nationale Supérieure des Mines de Paris)
Ecole doctorale:
Ingénierie des Systèmes, Matériaux, Mécanique, Energétique
PROFILE:
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
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starting date is 1st of March 2024 or on a mutually agreed date until the 1st of September 2024. Duration 36 months. Full-time paid position.- 29/02/202