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Machine learning approaches to distributed energy management

  • Full or part time
  • Application Deadline
    Tuesday, April 16, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Around a third of the world’s carbon emissions come from providing energy to heat, cool and power our buildings. Better demand-side management could help balance the load and therefore moderate energy use. Smart meters and intelligent controllers can both be used to this end, but the best rules they could use to manage this at the building level to result in system level efficiencies is not well understood.

This project will be to investigate distributed control with peer-to-peer network interaction between controllers. A simulation model will be developed to represent a network of households, connected by information exchange between neighbours in a community network.
Machine learning techniques will be used to try to discover locally optimal rules for balancing the load at the community and whole-system level.

Different AI/machine learning strategies will be investigated and compared to discover which perform most efficiently and reliably, as well as any shortcomings they may have. The ethical aspects of using self-taught artificial intelligent controllers on the needs and living conditions of occupants will also be investigated, along with the practical and ethical implications of sharing real-time energy use information between in-home systems.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found here: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Applicants must have a background in Engineering (civil, mechanical, electrical) and should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience (R, Python). Experience using building energy models (energy plus) and machine learning techniques (e.g. ANNs) would be a strong advantage.

Informal enquiries about the project should be directed Dr Nick McCullen at .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

References

https://arxiv.org/abs/1903.05137

How good is research at University of Bath in Architecture, Built Environment and Planning?

FTE Category A staff submitted: 28.38

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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