Data science for energy and environment

Providing reliable analysis and modelling to help mitigate climate change and pollution

The UK is undergoing a transition from the conventional carbon-emitting energy generation (using coal and gas) to an increased reliance on renewables because of the growing energy demand and pressures of climate change. By 2050, wind-generated energy is expected to provide 50% of UK energy needs. Various energy innovations in demand side response (DSR), such as shifting demand to off-peak hours and interventions for optimised energy use, have the potential to reduce our carbon footprint. Such innovations require quantification of carbon emissions and energy savings in diverse applications. Ranging from cloud-based household energy schedulers to corporate building management systems with micro-grids, the common goal is to reduce energy consumption. 

Our work includes:

• Analysis and modelling of novel energy interventions with estimation of energy demand and carbon emissions
• Development of scenarios for industrial energy installations, with cost, demand and emissions minimisation (such as Building Management Systems and fleets of electric vehicles)
• Data analytics for energy sensor networks with implementation of optimised control solutions
• Data analytics for renewable energy installations with estimation of early warning signals for infrastructural maintenance and reliable energy supply

We collaborate with academic and industrial partners on energy innovations with environmental impact, using real-world applications with large observational datasets and validated models based on physical understanding. We have modelled UK DSR programmes, analysed the demands of charging fleets of electric vehicles and supported asset management by simulating air flow within substations.

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