Mert Kesici

At consumer-side system operators suffer from lack of operational and situational awareness.

The current practice of consumption estimation is based on consumption profiles formulated in planning. This method is not sufficiently accurate. As the energy supply infrastructure is more stressed by high use of distributed generation (DG), an increasing number of unexpected disruptions can occur, which in turn increases the cost of integration. Though smart meters are available for monitoring the consumption, due to GDPR, the application is

The ESR will develop methods to estimate the system condition from the available information while fulfilling the privacy constraints. The work will identify and update the statistical patterns and correlations among the historical data, while scenarios can be derived without breaching the consumer’s privacy. Variants clustering will be used to identify the cluster. Kalman smoothing will be used for missing, unsynchronized or tampered data.

  • Host organisation: Imperial College of London, the United Kingdom
  • PhD-enrolment: Imperial College of London
  • Duration: 36 months
  • Expected start date: ~Nov 2021
  • Secondment: DEP (4 months), AIT (3 months)

Research Directors:

  • Prof. Bikash Pal
  • Dr. Anton Ishchenko
  • Dr. Stöckl Johannes