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Big data in energy

  • Full or part time
  • Application Deadline
    Sunday, July 05, 2020
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

With current energy systems generating ever-increasing amounts of data, there are new opportunities arising both in terms of economics and sustainability. This data spans a range of scales, from smart meter data in individual houses to demand and generation data from distribution network nodes and large-scale power plants respectively. The aim of this project is therefore to investigate the economic and sustainability opportunities arising from these data sources.

We will examine questions such as: How can consumers use their smart meter data to minimize their electricity bills? Can utilities exploit smart meter data to minimize their operational costs? How can these new data sources be used to best plan infrastructure investments and ensure future sustainability of the power network? In this way the project will operate across a range of scales and will be of interest to a number of stakeholders in the electricity systems. The project is also highly entrepreneurial in nature.

The project is likely to involve large amounts of programming and data-analysis. Candidates should have a strong interest in data analysis and visualization, as well as in energy and sustainability. Candidates will be expected to develop a strong knowledge of python or another language used in big data analysis and prior experience in one or more of these languages would be useful. A willingness to learn is essential.

How to apply

All applications are made online, please select the school/department name under the programme name section and include the quote reference number.

Entry Requirements

A relevant Master’s degree and/or experience in one or more of the following will be an advantage: programming, data analysis, python, data visualisation, energy analysis. Applicants who are unsure of their suitability are encouraged to informally contact the primary supervisor.

Funding Notes

This is an open call for candidates who are sponsored or who have their own funding. If you do not have funding, you may still apply, however Institutional funding is not guaranteed. Outstanding candidates (UK/EU/International) without funding will be considered for funding opportunities which may become available in the School.

UK/EU Fee band: Research Band 2 Laboratory Based (£4,327) for 2019/20
International Fee band: Research Band 2 Laboratory Based (£21,100) for 2019/20

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