<?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/40C2CE35-A64A-4D44-87F2-7C4FFEF64CF9" ns1:id="40C2CE35-A64A-4D44-87F2-7C4FFEF64CF9"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/78DC7BD4-A52F-4593-8D11-7F225B0B3B26" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F19058DF-D842-452C-B1AC-DDE5F24D14F1" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F19058DF-D842-452C-B1AC-DDE5F24D14F1" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2018-01-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/74C9AA0F-CD28-49E9-82FA-D0613C9F32B2" ns1:rel="FUND" ns1:start="2017-02-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">132723</ns2:identifier></ns2:identifiers><ns2:title>Automated diagnostics for Solar enabling 'power by the hour'</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>This early stage feasibility project is to research and evaluate the application of IoT-inspired machine learning

technologies to perform automatic diagnostics, improving the efficiency and productivity of solar sites. In

addition, a new business model potential enabled by this high level of automatic will be investigated.</ns2:abstractText></ns2:project>