Future Energy Associates

Published 17 March 2021 Last updated 17 March 2021

ORE Catapult’s Hackathon Winners

In October 2019, ORE Catapult ran its inaugural Hackathon in collaboration with ScottishPower Renewables and National Grid ESO. The challenge was complex, but the entrants rose to the task and delivered solutions that exceeded both the organisers and the judges’ expectations. All of the teams gave an excellent account of their achievements and methods; however, only one team was crowned the champion.

The team ‘Real Wind Turbines Have Power Curves’, made up of current and recently graduated students from the University College London Energy Institute, impressed the judges and claimed first place. The judges were particularly enthralled by the team inferring additional wind direction data from the available location and power output data to help create additional parameters for their machine learning algorithm.

Timothy Fletcher, Senior Asset Performance Analysis Engineer at ScottishPower Renewables, said:

“The team from University College London exceeded our expectations and were thoroughly deserving winners. They were able to identify the important features within the data and deploy novel data processing. They tested an impressive range of machine learning techniques within the time available and built a highly effective model to predict power availability. In the final assessment, they presented their efforts with enthusiasm, clarity and an obvious sense of teamwork”.

Future Energy Associates is Formed

Fast forward to today, and the ORE Catapult Hackathon winners have formed Future Energy Associates (FEA). In a world of ever-growing complexity, FEA helps extract insights from owner/operator’s energy data and build solutions for their energy analysis workflows. With expertise in price optimisation, power forecasting and curtailment, FEA is determined to ensure offshore wind farm owner/operators make the most out of their assets. They are currently working on spatial correlation of curtailment in the UK market and developing predictions of power available of wind farms.

As the whole energy sector increasingly realises the value of machine learning and big data techniques to improve the cost reduction, FEA is ideally placed at the intersection of data science and energy domain knowledge. In the year since the company’s formation, FEA has completed projects ranging from:

  • wind farm design optimisation;
  • monitoring electric vehicle uptake;
  • using remote sensing to predict industrial emissions;
  • data engineering for satellite data ingestion;
  • assisting the Energy Systems Catapult with Net Zero capability mapping;
  • investigating marginal carbon signals; and
  • winning a second hackathon on PPA aggregation organised by Zeigo.

ScottishPower Renewables Projects

Having impressed ScottishPower Renewables at ORE Catapult’s inaugural Hackathon, FEA has established a flourishing relationship with the energy industry giant to explore aspects of site layout optimisation and cable route optimisation for offshore wind.

ScottishPower Renewables initially contracted FEA to develop a custom software tool to optimise the layout of offshore wind farm turbines for a given site. This involved liaising with wind resources, GIS and engineering teams to understand the problem and the users of the tool; processing varied input data sources and allowing customisable variables; interfacing with other industry-standard software, and developing an algorithmic approach to explore possible permutations for a wind farm layout and presenting the results.

A small decrease in capital and operational expenditure costs from a more optimal layout translates to millions of pounds saved over a farms’ lifetime, so the return on investment from this initial project was very positive.

David Bridle, Senior GIS Engineer and project manager at ScottishPower, said:

“Our collaboration with FEA has been a great success. Through this partnership, we have been able to develop and build upon our existing set of tools, workflows, and business intelligence and apply cutting-edge data science approaches to offshore wind farm optimisation.”

The relationship is continuing as FEA is working with ScottishPower Renewables on a follow-up project.

Future Steps

FEA has taken on a recent graduate from UCL’s Energy Systems and Data Analytics MSc and will continue to expand to meet the energy sector’s growing needs. As the UK’s wind sector goes from strength to strength, the opportunity and value proposition for data science and machine learning will only increase. FEA intends to strengthen its relationship with the sector through direct consultancy projects and software tools.

Contact Our Team

Alex Louden

Senior Technology Acceleration Manager

Email Alex Louden

+44 (0)141 559 7051

see bio