<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/CBC0DC9A-C495-47C4-90C0-95AD96B2F59E" ns1:id="CBC0DC9A-C495-47C4-90C0-95AD96B2F59E"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/0AA49640-2F3B-4FDB-9BBA-D21B34AFE5EF" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/422ED10C-6175-42A7-9702-A6CA03B51670" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/1325B689-9BF8-487D-B5C2-E7CA89E60C4A" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/77EACCD3-271A-4497-9860-1DA44DAE32BF" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/D53CD2A0-563A-4D3E-BF91-4908A562C9B5" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/422ED10C-6175-42A7-9702-A6CA03B51670" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/9DCC86B0-B759-41B9-9EA6-2F319BCD0F94" ns1:rel="FUND" ns1:start="2025-08-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10159317</ns2:identifier></ns2:identifiers><ns2:title>Shore Power Technologies assisted with AI and Machine Learning Approach</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>This project will explore how ports can provide clean, reliable power to the next generation of hybrid and electric vessels. With demand growing rapidly for Service Operation Vessels (SOVs) to support offshore wind farms, there is an urgent need for smart charging solutions that reduce emissions while ships are docked.

Led by a consortium including Ameresco, GeoPura, Cranfield University, Port of Tyne, and M3MAS, the study will assess how to combine green technologies---such as grid power, batteries, hydrogen, and methanol---into flexible, modular charging systems. These systems could help ports meet the UK's ambitious maritime emissions targets and support the transition to net-zero by 2050\.

The team will use digital twins and AI to model energy use, optimise vessel charging schedules, and design efficient supply chains for alternative fuels. The goal is to make it easier and more cost-effective for ports to invest in low-carbon infrastructure.

The findings will inform a full-scale demonstration at Port of Tyne and provide a roadmap for other ports across the UK. If successful, the solution could cut ship-side emissions at berth by up to 100%, helping to clean up the air around ports and position the UK as a global leader in green maritime innovation.</ns2:abstractText></ns2:project>