Image: Demand forcasting graph from Team White on Electricity Demand and On Site wind forecasting
Our Energy Systems and Data Analytics MSc students have published online their ideas for applying data science and machine learning within the energy systems as part of their ‘Energy Data Analytics’ module. The students formed teams of four and worked for about 5 weeks to develop their own idea.
Dr Aidan O’Sullivan, Programme Lead for Energy Systems and Data Analytics MSc, said:
This year’s projects have been some of the most creative and diverse ideas we’ve seen on the course, from using machine learning to predict changes in sea-ice to finding the best locations for EV chargers in London. The progress students have made in the first term has been exceptional and it’s really rewarding to see the data science and machine learning skills they’ve worked so hard to develop used to explore their interests and tackle decarbonisation challenges and climate change issues. Special thanks to last year’s graduates Oliver Paul, Tom Falconer and Felipe Riquelme Larenas, who mentored the groups throughout their projects.
Student's projects
Team Blue: Rapid charger placement for electric taxis in Greater London: A density driven approach
In the context of Greater London's goal of having an 100% electric taxi fleet by 2033, this project sought to locate a network of rapid charging points in London accordance with taxi charging demand. Using a weighted cost function and greedy algorithm that accounted for taxi density, users' time cost, distance to the nearest charging point, and the cost of electricity generation, our research was able to reasonably estimate the number and placement of charging points for current and future levels of charging demand.
View Team Blue's project online
Team Green: German solar data analysis
View Team Green's project online
Team Pink: EPC Ratings in Greater London and fuel poverty
This project aimed to determine a spatial relationship between low building efficiency, represented by a building's EPC rating, and high fuel poverty rates in Greater London. Students used data from the London Building Stock Model (Created by UCL Enegry Institute for the Greater London Authority). Using a combination of data for buildings with and without EPC ratings they developed machine learning (ML) algorithms to help predict the EPC rating of uncertified buildings. They then used the location data of the known and predicted EPC ratings to identify regions in Greater London that suffer from high rates of fuel poverty and consist of higher concentrations of inefficient buildings. Once these regions were identified, further investigations could be carried out to determine if poor building efficiency contributes significantly to its fuel poverty.
View Team Pink's project online
Team Orange: Arctic Ice Forecasting
View Team Orange's project online
Team Brown: Electricity Demand Forecasting
View Team Brown's project online
Team White: Electricity Demand and On Site wind forecasting
Energy Systems and Data Analytics MSc
Energy Systems and Data Analytics MSc teaches student the skills and knowledge to unlock the transformative potential of big energy data, and understand how it can reshape the energy sector.
The Bartlett School of Planning visit Shanghai to expand collaborations in China
The Bartlett School of Planning (BSP) Director, Prof. Raco and Research Director Prof. Andres spent a week in Shanghai to launch new projects and forge new partnerships.
23 Apr 2025
IEDE TALKS 2025: Prof Ian Durbin in conversation with James Ford (Hoare Lea)
James Ford, Director at Hoare Lea, joins IEDE Talks 2025 to share insights on innovation, sustainability, and thriving in future careers across the built environment.
23 Apr 2025
Opinion: Generative AI is reshaping the global economy — Are we ready for the consequences?
Jo-An Occhipinti, William Hynes (IGP) and Ante Prodan on why generative AI demands a new social contract
23 Apr 2025