Any views expressed within media held on this service are those of the contributors, should not be taken as approved or endorsed by the University, and do not necessarily reflect the views of the University in respect of any particular issue.
Press "Enter" to skip to content

Funded PhD position in Machine Learning for Energy System Integration

In addition, to the two RA positions (one closes today and the other on Friday) we have now also a funded PhD position open in

Machine learning methods to manage the integration of heating systems into the wider energy system

Heat demand which has large seasonal variations and high morning peak ramp-up rates, is responsible for 44% of the total energy demand in the UK and mainly supplied through the natural gas grid. District energy systems with Seasonal Thermal Energy Storage (STES) can be affordable and more sustainable alternatives that can handle the high ramp-up rates and seasonal variations. However, existing systems are designed and operated independently from the wider energy system (electricity, cooling, industry and transport sectors), while the best solution (in terms of emissions reduction and cost) can only be found if all energy sectors are combined and coordinated. This multi-sector integration is an open challenge due to the nonlinear interactions between the different sectors as well as the significant computational complexity due to required spatial and temporal resolutions and model complexity.

In this project, the successful candidate will develop, implement and apply machine learning methods for the design and optimisation of district heating system with STES as part of the wider energy system. While the main focus is on using machine learning based surrogate models to link detailed CFD simulations with whole system models, there is scope to investigate other areas such as system control and demand/supply predictions.

The candidate will develop a wide range of skills in heating systems with STES design and machine learning methods which will be widely applicable to the candidate’s future career. The project is linked to the EPSRC funded INTEGRATE project and the PhD student will be jointly supervised by Dr Daniel Friedrich at the School of Engineering at the University of Edinburgh and Prof Ben Hughes at the University of Hull.

Closing date for applications: Position will remain open until filled.
Expected studentship start date: 1st October 2020 or as soon as possible thereafter.

Links:
https://www.eng.ed.ac.uk/studying/postgraduate/research/phd/machine-learning-methods-manage-integration-heating-systems-wider

Leave a Reply

Your email address will not be published. Required fields are marked *

css.php

Report this page

To report inappropriate content on this page, please use the form below. Upon receiving your report, we will be in touch as per the Take Down Policy of the service.

Please note that personal data collected through this form is used and stored for the purposes of processing this report and communication with you.

If you are unable to report a concern about content via this form please contact the Service Owner.

Please enter an email address you wish to be contacted on. Please describe the unacceptable content in sufficient detail to allow us to locate it, and why you consider it to be unacceptable.
By submitting this report, you accept that it is accurate and that fraudulent or nuisance complaints may result in action by the University.

  Cancel