For his undergraduate degree Ayrton studied Chemical Engineering at the University of Sheffield, focussing on the application of carbon capture in heavy industry. As part of his degree he carried out a research project quantifying how renewables were reducing wholesale prices for electricity. This gave him a window into the vast world of energy systems modelling and started his hunt for a Master’s on the topic. When UCL’s Energy Systems and Data Analytics MSc was launched, he said knew he had to apply.
When asked why he chose UCL he responded:
My previously studies had felt very siloed, studying at the Bartlett offered the opportunity to remedy this and place my work in the context of wider changes across society and the energy sector.
Ayrton said he was drawn by the strong connections with policy makers and industry held by the Bartlett School of Environment, Energy and Resources, UCL-Energy’s home in The Bartlett Faculty of the Built Environment. He also noted the quality and volume of research the department outputs.
Ayrton joined the first cohort of Energy Systems and Data Analytics MSc students at UCL in the 2018/19 academic year. On the course's content he said:
Alongside providing exposure to a very wide range of different tools that can be used for energy data science, the course also added a lot of depth in particular to statistical methods for spatial and time series analysis. There was also a strong focus on collaborative programming, which was something I hadn’t done much of before but since graduating has become increasingly useful
He also enjoyed the extracurricular Energy Innovators seminar series. This series included talks from several heads of data science at energy companies and open-source organisations.
Hearing from professionals driving change in the industry through similar techniques to what we were learning was particularly motivating.
Ayrton wrote his dissertation on applying deep learning techniques to short-term price forecasting in the UK imbalance market, which is used to ensure that the supply of electricity perfectly matches demand. This involved a combination of modelling physical aspects of the UK system such as the generation from renewable sources, as well as market-based effects as well as market-based effects like firm concentration. At the end of his dissertation he felt as though he had only scratched the surface of the topic, so he applied for a PhD at UCL Energy Institute. He has been working on this, with our Energy & Artificial Interlligence Lab since September.
For anyone thinking of studying Energy Systems and Data Analytics MSc, when asked if he would recommend this course Ayrton said:
100%. The course is one of a kind in terms of teaching the core components of data analysis, statistics and energy modelling, but more importantly this is framed within a whole systems approach for the energy sector. I’d recommend it for anyone interested in turning industry’s large volumes of under-utilised data into models and insights that can help further the energy transition.
He advises future students to:
Look into the areas of the energy sector that you’re particularly keen to make changes in and research how data science is being used to improve them. The programme will then give you the skills you need to answer your own questions and a great environment to work within.
We like to thank Ayrton for sharing his views with us and wish him all the best with his PhD.
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