The Institute of Science & Technology’s AI Special Interest Group is delighted to announce the next in its series of seminars and welcomes Jay Flynn from Swansea University.
As Electric Vehicles (EVs) emerge as the dominant form of green transport in the UK, effective infrastructures to support their uptake are crucial. In this seminar, we present a novel approach to automate surveying of the built environment utilising deep learning and geospatial analysis to identify residential properties suitable for EV charging. The significance of this methodology extends beyond its ability to support the transition to an EV-dominated market. By automating surveying techniques using streetscape imagery, the proposed methodology can be easily adapted to a wide range of potential use cases to survey the external features of properties. In this seminar, we demonstrate how the derived data from the proposed workflow can be used to identify optimal locations for public EV charging infrastructure, predict consumer trends and target marketing. Overall, the proposed methodology has the potential to not only support the transition to an EV-dominated market but also to create new opportunities for data-as-a-service business models across various industries.
Dr. Jay Flynn is a machine learning engineer whose research interests include the implementation of anomaly detection, computer vision, predictive maintenance, layout optimisation, and other machine learning solutions in the automotive industry.
Some of the exemplary projects that Dr. Flynn has delivered include:
- Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging
- Anomaly Detection of DC Nut Runner Processes in Engine Assembly
- Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
- Factory Layout optimisation using genetic algorithms