Predictive models of Renewable electricity generation and electricity consumption profiles through Fourier Transform analysis
Predictive models of Renewable electricity generation and electricity consumption profiles through fourier transform analysis
Semester/Master project
Fall 2025
Context
Today’s energy supply still heavily relies on fossil fuels, releasing greenhouse gases and damaging ecosystems. The society is moving to renewable alternatives like solar and wind energy, which are promising for decarbonized electricity production. Yet, they require balancing intermittency with storage technologies such as Battery systems or power-to-X-to-power devices, where X represents storable molecules for future power generation. In this context, developping a predictive model for RE electricity production and consumption profiles through fourier transform is a very powerful approach. Indeed the representation into key frequency components of these highly intermittent, yet periodic signals can enable:
- Predictive modelling for both production and consumption based on geographical, economical and societal features.
- Dimensionning of storage technologies based on their intrinsic characteristics such as storage efficiencies and self-discharge rates (Battery: short-term, rSOC: long-term)
This framework would provide a fair and data-driven comparison of storage technologies and their potential around the world based on few parameters only.
The Tasks
The project consists of the following main steps:
- Understand the developed methodology for RE-electricity generation profiles.
- Gather worlwide data and apply the methodology to the consumption profiles.
- Consider different storage facilities and infrastructure and handle multi-period optimization.
- Evaluate world-wide storage potential.
- Optionally, finalize and certify matrix-based fourier-space optimization approach for storage systems.
Skills
Basics of signal processing
Python programming
General knowledge on energy system modelling and Optimization
Interest in storage technologies.
Lectures:
- Energy Conversion and Renewable Energy
- Applied Data Analysis (recommended, not required)
Practical information
This study can consist of a 2-steps approach, starting with a semester project and following up with a Master thesis (if interest and dedication). It can also directly be a Master project.
The project will be conducted in IPESE lab in Sion (EPFL Valais) at about 1h05 from Lausanne train station. Possibility of meeting on the campus in Lausanne (mainly Mondays) once the project is up and running.
Any transportation fees between Lausanne and Sion will be reimbursed by the lab. English is required for the end report, but the supervision can be carried out in french, english or german.
Supervisor
The project will be supervised by Arthur Waeber. If interested, please contact me via e-mail attaching your CV and a short motivation letter.
mailto: arthur.waeber@epfl.ch;