PhD Position Digital twin for data driven control, commissioning and continuous design optimisation…

The backbone of the PhD is a toolchainthat constructs a digital twin of the PEN, relying on capturing operationaldata and subsequent automated data analytics. In the setup of such digitalbackbone for a PEN, the PhD student will research amongst others:

·        The type of operational datarequired [at appliance level, building level, street level,…] and theirtechnical characteristics such as frequency, granularity and precision

·        Protocols to gather such datairrespective of vendor and owner of the equipment

·        GDPR proof methods to handledata at PEN level (scalable privacy-preserving big data aggregation: respectprivacy while still having maximum insights from data at lower granularity)

·        Methodological approaches totrain the digital twin model by the gathered data (initial training), andmethods to iteratively update the digital twin of the district

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

The research will be based on past andcurrent research at the Building Physics and Sustainable Design Section at KULeuven and the SEB DEMO team (Unit Sustainable Energy and Built environment) atVITO. There will be a close collaboration with other researchers from EnergyVille.