UCL’s Energy Institute is seeking applications for a fully funded Studentship in: ‘Developing an open access tool to generate synthetic occupant profiles from smart meter data for building stock modelling‘
This project aims to develop an open access tool to generate synthetic populations of building occupant profiles using smart meter data. The tool will enable the generation of high temporal resolution profiles based on building type, location, locality, socio-economic status and other contextual factors.
Supervisors: Professor Paul Ruyssevelt, Dr Pamela Fennell and Dr Olly Smith of UCL Energy Institute and Dr Stephen Watson of Loughborough University.
Funding: The studentship will cover UK course fees and an enhanced tax-free stipend of approx. £22,000 per year for 4 years along with a substantial budget for research, travel, and centre activities.
Fees: ERBE CDT has very limited funding for applicants requiring coverage of overseas fees. We advise all interested applicants to be familiar with the changes to EU and International Eligibility for EPSRC/UKRI funded studentships
Dates: 4 years from September 2023
Context and Project description:
Models of buildings and their energy consumption are increasingly important tools for predicting electricity loads particularly when combined with models of local electricity distribution networks. However, for these models to be useful they need to be based on realistic predictions of how people use energy in buildings.
Recently researchers have proposed the development of occupancy inputs for building energy simulation using smart meter data. These approaches are promising but limited by the small datasets available. The Smart Energy Research Lab data set offers a unique opportunity to expand on such approaches and develop a tool which will revolutionise the characterisation of occupant actions in urban scale building energy models.
The research to be undertaken on this project is likely to include:
- Feature extraction: Extraction of relevant features from the SERL data set
- Machine learning methods: Time-series machine learning methods, clustering techniques, and other analytical tools will be used to analyse the data and develop an algorithm to produce a population of appropriate high temporal resolution profiles.
- Integration with building stock models and deployment: The usage profiles will be integrated with physics-based building stock models, thus improving the accuracy and reducing uncertainties in the predictions.
Studentship aims:
The aim of this studentship is to develop an open-access software tool which can be used by building simulation professionals and researchers to generate realistic, high-resolution profiles of occupant-related inputs.
Person specification:
The successful candidate will have a degree in a quantitative subject and experience of computer programming.
A minimum of an upper second-class UK Bachelor’s degree and a Master’s degree, or an overseas qualification of an equivalent standard, in a relevant subject, is essential. Exceptionally: where applicants have other suitable research or professional experience, they may be admitted without a Master’s degree; or where applicants have a lower second-class UK Honours Bachelor’s degree (2:2) (or equivalent) they must possess a relevant Master’s degree to be admitted.
Applicants must also meet the minimum language requirements of UCL
Applicants should be familiar with the changes to EU and International Eligibility for UKRI funded studentships.
How to apply
All CV’s and Cover Letters must be completely anonymised and not contain any references to protected characteristics, such as gender, ethnicity or race.
Please submit your application by email to the UCL ERBE Centre Manager (bseer.erbecdt@ucl.ac.uk) with Subject Reference: 4-year PhD studentship in ‘Developing an open access tool to generate synthetic occupant profiles from smart meter data for building stock modelling‘
The application should include each of the following:
1) An anonymised Cover Letter clearly stating why you are applying and how your interests and experience relate to this project, and your understanding of eligibility according to these guidelines: EU and International Eligibility for EPSRC/UKRI funded studentships
2) An anonymised CV
3) Complete the CDT EPSRC Eligibility Questionnaire and EDI Questionnaire via the linked Microsoft Forms.
Only shortlisted applicants will be invited for an interview.
• For the interview shortlisted candidates will be asked to show proof of their degree certificate(s) and transcript(s) of degree(s), and proof of their fees eligibility.
• The interview panel will consist of the project’s academic supervisors at UCL and a representative of the ERBE CDT Academic management. The interview will include a short presentation from the candidate on their ideas of how to approach this PhD project.
Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme for ERBE CDT.
Deadline for applications: 31st May 2023 @ 23:59 (UK time)
Interviews week commencing: TBC
For further details about the admission process, please contact: bseer.erbecdt@ucl.ac.uk
For any further details regarding the project, contact Dr Pamela Fennell, pamela.fennell@ucl.ac.uk
You will be undertaking this project:
- In UCL at the main (Bloomsbury) campus as part of the EPSRC-SFI Centre for Doctoral Training in Energy Resilience and the Built Environment (ERBE CDT). This is a collaboration between UCL, Loughborough University and Marine and Renewable Energy Ireland (MaREI). For more information please see http://erbecdt.ac.uk