Climate change is a critical issue requiring significant changes across sectors and disciplines, particularly around energy. Today (20 September 2019) a range of institutions are striking to highlight their call for more drastic climate action. This group includes many academic, construction, design and digital organisations. 

The strikes follow the Intergovernmental Panel on Climate Change (IPCC) report, which states that to avoid overshooting a 1.5oC rise, global net anthropogenic CO2 emissions need to decline by 45% from 2010 levels by 2030, reaching net zero by 2050. One view is that we now need to collaboratively clarify the detail of how each sector gets there. Crucially, this will need to be done while minimising unintended consequences.

Take our built environment for example -  buildings and construction account for 39% of global CO2 emissions.  As a researcher on energy use in buildings, a field which has grown significantly since Climate Change Act (2008), I am aware of the challenge. My research at the Turing explores how we can better use available data to inform decision-making for building retrofit, stemming from work on energy, comfort, process and installation factors.

There are other data-related energy and urban projects at the Turing addressing challenges related to tackling climate change. For a small snapshot (in no way an exhaustive list):

  • Rebecca Ward of University of Cambridge, working with Ruchi Choudhary is using statistical techniques to improve the accuracy of building energy simulation. Rebecca previously developed a bespoke simulation tool for Kew Gardens to assist in planning a reduction in their energy demand.
  • Pei-Yuan (Leo) Hsu of Imperial College London recently published a paper on developing tools to improve the efficiency of supply chain processes for construction projects, this could lead to lower CO2 emissions at both construction and building use stages.
  • Moving towards a smarter energy grid, Sustainable Infrastructures, connects remote sensing with online data analytics to quickly identify failure, diagnose and plan recovery. City wide, Theo Damoulas of University of Warwick is using Bayesian statistics and machine learning to process online urban data streams.
  • On solar energy, Andrew Duncan and his team contributed to new solar energy forecasting models for the National Gird, achieving a 33% increase in accuracy of day-ahead solar forecasts.  In addition, Dan Stowell and his team are establishing an open data store for solar photovoltaic (PV) geodata, combining data from government, crowdsourcing, and satellites.

These are all exciting developments. One of my personal highlights working at the Turing so far has been meeting James Geddes, a leading data scientist in the Research Engineering team who worked on the original ‘2050 Energy Calculator’, inspired by the late Sir David Mackay’s book ‘Sustainable Energy Without the Hot Air’. This happens to be a tool and text we used in training at the Lo-Lo EPSRC CDT in Energy Demand.

One of the Turing university partners is the University of Exeter, the academic home of Peter Hopkinson who is doing excellent work at the Centre for Circular Economy. Exeter recently hosted the first Summit of Environmental Intelligence - researchers interested in data science and AI approaches to tackling environmental challenges can join the Environmental Intelligence Network. 


At the Turing, we are open to discuss potential collaborations with researchers and businesses looking to better use existing datasets and accelerate action to minimise climate change. If this is of interest, get in touch with Eirini Malliaraki via [email protected] or Shana Tufail via [email protected] and they will be able to direct you to a relevant researcher or a member of our partnerships team.