Vacancy No. IAI 09-19

IAI 09-19 Masterthesis: Gaussian Process Regression for Electric Load Forecasting

Job description

Gaussian processes are a powerful methodology from machine learning for regression problems: given some (often time-dependent) data, find a function that predicts the value of the quantity at a not yet seen time instant. Gaussian processes provide not just a point forecast, but an entire probabilistic forecast in terms of Gaussian normal distributions. The objective of the research project is to take real electrical power consumption data measured at Campus North, and to design a suitable covariance kernel and predict future power consumption. The design of the covariance kernel should include seasonal fluctuations and day/week-day discrepancies. The implementations can be done within the research platform EnergyLab~2.0 at Campus North. Having obtained a Gaussian process for the electrical load(s), this can be employed in optimization-based scheduling of power systems---which shall be studied if time permits.

If you have a background in a scientific programming language such a Julia, Python, or Matlab, and if you are interested in learning about Gaussian processes, and if you are interested in applying methods to data from the real world, then you are the perfect person.

Personal qualification

applied mathematician, electrical engineer, or mechanical engineer with a strong background in machine learning, numerical optimization, and/or scientific computing in Python, Matlab, Julia

Organizational unit

Institute for Automation and Applied Informatics (IAI)

Starting date

as soon as possible

Contract duration

as needed

Contact person

For furtherinformation please contact Tillmann Mühlpfordt 0721/ 608-26459, tilmann.muehlpfordt@kit.edu

Application

Please apply online using the button below for this vacancy number IAI 09-19.
Personnel support is provided by 

Ms Schaber
phone: +49 721 608-25184,

Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

If qualified, severely disabled persons will be preferred.