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

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.

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.

This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”. The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is divided into three main themes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.

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.

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 as soon as possible in 2024. Duration 36 months. Full-time position.

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