PhD position Digital twin for data driven control, commissioning and continuous design optimisation of positive energy neighborhoods

Job description

Positive Energy Neighborhoods (PENs) are energy-efficient and energy-flexible urban areas or groups of connected buildings which produce net zero greenhouse gas emissions and actively manage an annual local or regional surplus production of renewable energy.

This PhD will develop a methodology for generating a digital twin of a PEN which is trained and continuously improved by analysing the operational data of an existing neighborhood. Informed by this digital twin, a method is developed to continuously evaluate the performance of the PEN and suggest changes in the control strategy or future investments in the technology mix of the PEN.


The backbone of the PhD is a toolchain that constructs a digital twin of the PEN, relying on capturing operational data and subsequent automated data analytics. In the setup of such digital backbone for a PEN, the PhD student will explicitly research:

  • The type of operational data required [at appliance level, building level, street level,…] and their technical characteristics such as frequency, granularity and precision.
  • Protocols to gather such data irrespective of vendor and owner of the equipment.
  • GDPR proof methods to handle data at PEN level (scalable privacy-preserving big data aggregation: respect privacy while still having maximum insights from data at lower granularity).
  • Methodological approaches to train the digital twin model by the gathered data (initial training), and methods to iteratively update the digital twin of the district.

The digital twin will be used to continuously monitor the performance of the district under study and compare this to the predicted performance goals. The PhD student will develop a proof-of-concept application to support the management of the PEN. This application will be able to suggests adjustments to the PEN control strategies (controlling multiple assets such as local storage, EV charging, etc. and multiple energy vectors) and allows to evaluate the impact of changes in the PEN technology mix.


Our offer

VITO and KU Leuven offer a PhD scholarship to the candidate for 4 years (2+2). Globally the intention is for the candidate to spend 50% of their time in EnergyVille, Genk. University affiliation for this PhD will be the Building Physics and Sustainable Design group at the KU Leuven, a group with extensive expertise in on building physics and building and district energy performance simulations. The KU Leuven promotor for his PhD will be Prof. Dirk Saelens.

At VITO, the PhD candidate will work within the SEB DEMO team (Unit Sustainable Energy and Built environment; team District Energy Modeling), under the supervision of Dr. Stijn Verbeke.

How to apply?

Applications should be submitted online and should include a copy of your CV and a cover letter. 

You can apply for this PhD vacancy no later than October 24, 2022.

For more information, please contact dr. Stijn Verbeke (stijn.verbeke@vito.be).

Job requirements

  • You hold a Master's degree with distinction in engineering sciences (civil engineering or master energy), architectural engineering, or applied engineering / data science engineering with an affinity for research on buildings and energy systems.
  • You can work independently, as well as in a team.
  • You are motivated, creative, and are interested by both the academic and valorization aspects of research on Positive Energy Neighborhoods.
  • You are fluent in English, both oral and written.
  • You are eager to learn and to disseminate your research results in the form of scientific articles or communications at conferences.