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

30 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 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 flexibility-aware, 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:  

The first step of this thesis will be to characterize and define typical patterns of energy demand at local level as a function of the aggregation of consumers (from single households up to tens or hundreds of consumers). Then existing load forecasting methods will be applied to estimate the reference performance that can be achieved. Several sources of input data, like smart meters data, weather forecasts, EV charging information etc. will be considered. The sensitivity of the existing methods on missing data will be studied together with the contribution of spatiotemporal information (i.e. measurements from neighbor smart homes with similar consumption profiles). It will be studied what are the requirements of the considered and future applications (use cases) for forecasting models that have to be taken into account in the design of such models. I.e. if they have to operate autonomously (i.e. at the “edge” or at automats with minimal maintenance and tuning requirements), if they have to be adaptive to structural changes of the consumption (i.e. addition of new usages), their robustness on missing data, their sensitivity on personal information a.o. In a next step, the contribution of NLP (natural language processing methods) methods will be assessed. Existing models developed at PERSEE for prediction at regional and national level will be considered. These methods will extract explanatory variables from text and audiovisual sources that are potentially informative for local energy consumption. Language data generated by AI will be integrated into the NLP methodology, which requires potentially AI detection and ethics analysis. Representative case studies will be provided by ongoing projects and will prioritize open-source datasets. A second step will be dedicated to the dynamic adaptation of the forecasting approach to massive penetration of flexibility in energy networks, e.g. from the electric distribution and transmission systems. This means to develop a prediction of the evolution of local demand before, during and after flexibility activations of different characteristics, durations and amplitudes.

 

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 paid position.

 

For more information please contact Prof. Georges Kariniotakis and Dr Simon Camal (emails below) 

 

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/119787

Contact

Website

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

STATUS: EXPIRED