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Fully funded PhD Studentship in Artificial intelligence Methods For Electricity Market Imbalance Prediction

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  • Full or part time
    Dr A O'Sullivan
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

UCL Energy Institute ( invites applications for a fully funded 4-year PhD studentship covering UK/EU fees plus stipend. It will focus on the development of Artificial Intelligence based methods to improve predictions of electricity market imbalance volumes and price, to enhance the stability of the system and enable better management of electricity markets by system operators.


Electricity market reform has been one of the key tools used to enable a transition to a decarbonised energy system. While reform has supported the growth of renewable generation and less carbon intensive electricity generation they have also made the system vastly more complex. This has led to greater volatility and uncertainty, particularly in the balancing market, which is the mechanism by which the System Operator meets shortfalls/excess of generation at short notice. This market is particularly hard to predict and is a significant producer of carbon emissions and also cost to the system, the mechanism was estimated to cost the UK £1billion in 2017 and this is forecast to increase in cost with greater penetration of renewables.


The proposed PhD topic involves the development of Artificial Intelligence approaches that can make use of diverse and disparate data to develop month, week and day ahead forecasts of the imbalance volume/price in the market per half hourly period. The research will also involve estimating the level of uncertainty around these predictions and developing an understanding of electricity market structure and interpreting findings to promote better market design. In your PhD you will be expected to develop expertise in Artificial intelligence methods from reinforcement learning and neural networks to Bayesian deep learning. The project is highly interdisciplinary one requiring an understanding of power systems, market design and advanced artificial intelligence.

Person Specification

• Excellent numerical and computing skills. Passionate about quantitative analysis and conducting research.
• An MSc/Bachelors degree in engineering, statistics or computer science or other relevant quantitative disciplines.
• Interest in the challenges facing the energy sector and the low carbon transition
• Knowledge of relevant programming languages, (such as R, MATLAB, Python)
• Ability to use own initiative, prioritise workload and work as part of a team in the Energy and AI group

Application Procedure

Stage 1 - Pre-application documents - (1) CV, (2) academic transcripts, and (3) 1-page personal statement outlining motivation, interest and eligibility for the post - should be emailed directly to the PhD Administrator,Teresa Dawkins: [Email Address Removed] with ESPRC application in the subject field. Stage 2 - Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme.

Any offer made will be subject to references and proof of meeting the UCL English language requirements (

Informal enquiries on the content of the research topic should be emailed to Aidan O’Sullivan, [Email Address Removed]

Deadline for application: 27 April 2019
Interviews week commencing 14 May 2018

Funding Notes

Fully funded 4-year PhD studentship covering UK/EU fees plus stipend - Stipend: £17,009 plus fees of £5,210

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