AI enabled EV charge point location optimiser

The UK is the first major economy to pledge net zero carbon emissions by 2050\. The achievement of this goal will require transformation in the energy sector. The application of cross-sectoral geospatial data, which combines location information with attribute and temporal information, can 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 and locations \[rural and urban\], and user profiles, has a range of implications that are difficult to model with conventional approaches.

We propose the creation of 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 charge point placement.

We will address these challenges using our world-class expertise in Bayesian optimization and Gaussian Process (GP) models. GPs can handle low data regimes and provide values even in the presence of missing and partial information. Key outcomes of this project will be to address data usability to support geospatial modelling, which in turn will support decision-making across a range of stakeholders.

Our proposal to combine disparate, exogenous, and unstructured data sources with geospatial data is not unique. However we are the first to use GPs with geospatial data, giving superior adaptability, accuracy, interpretability and explainability. Using GPs will significantly improve the accuracy of our tool versus the state of the art competitors. The successful application of this approach will enable more quantitative and targeted local planning and prioritising of resources.

The successful transition to net zero also requires extensive collaboration with a broad set of stakeholders. A key feature of our proposed product is that it will enhance human-AI collaboration by providing an interpretable decision making tool for a range of different users, including: Local Authorities (LAs), Charge Point Operators (CPOs) and Distribution Network Operators (DNOs). Bringing all these stakeholders together is the first step towards the development of a strong business case that can drive investment in the sustainable energy sector. We have already taken initial steps in this process by engaging with Oxfordshire-County-Council, Zeta who support our application.