Geospatial solution for EV Chargepoint Infrastructure

7f8f5a8a-7884-4918-aa5b-0a05e572cd63

Closed

ISCF
ISCF
ISCF
ISCF
ISCF

£2,227,700

Aug. 31, 2021

May 31, 2022

The UK is the first major world economy to pledge net zero carbon emissions by 2050\. The achievement of this goal will require transformations in a number of sectors. To this end, sectors such as transport and urban planning, have embarked upon a nimble, focused and data-driven approach in addressing their challenges. The energy sector can undertake a similar approach, taking data from the energy sector and additional sectors , such as transport, to deliver targeted solutions at the local level.

We intend to use geospatial data, which combines location information with attribute and often temporal information, to contribute to the evolution of the distributed energy paradigm.

The ability to analyze sparse or incomplete data with respect to, for example smart meters, electric vehicle (EV) uptake, EV charging type, locations and user profile, has a range of implications that are difficult to model with conventional approaches.

We are building an energy-focused geospatial system that will enable the user to visualise overlays of multivariate spatially and temporally varying data, model and predict trends and correlations, infer across areas of sparse data collection, and model the effects of changes on the system such as varying supply, demand or infrastructure. It will further allow for the simulation and testing of different strategies, for example, alternative charging point placement.

We will address these challenges using our world-class expertise in Bayesian optimization and scalable probabilistic models. Using a probabilistic approach will significantly improve the accuracy of AI models, over existing AI based tools. The successful application of this approach will enable the local energy sector to be more quantitative and targeted in its planning and prioritising of resources.

The successful transition to net zero local energy systems requires not only cross-sectoral data, advanced geospatial and machine learning models and techniques, but also, crucially extensive collaboration with a broad set of stakeholders to properly understand their needs.

We have already taken some initial steps in this process. For example, with respect to the EVs, we are in dialogue with local authorities and commercial organizations who are currently looking for immediate support with respect to the analysis of EV charger types, optimised roll-out of EV charging points and the implications for the capacity of local energy networks.