<?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/974608AA-219D-44B5-9DCD-012A65CD8277" ns1:id="974608AA-219D-44B5-9DCD-012A65CD8277"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/7192A720-BECE-4E89-806D-7EEF4F1B4B26" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/CAF6B300-ABAC-4E87-8775-04060C1560E2" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/CAF6B300-ABAC-4E87-8775-04060C1560E2" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2017-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/1014A1A3-2AB5-405D-9799-57121975868F" ns1:rel="FUND" ns1:start="2017-03-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">132450</ns2:identifier></ns2:identifiers><ns2:title>Quality of Service for Heat Networks</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Is it feasible to infer what a consumer requested and a district heating network delivered using high frequency 

data from existing heat meters? 

 Our feasibility study will process heat meter data from our 24 home smart network to infer what the user 

requested, then compare the estimate with the actual values from 4 million datapoints it already collects and 

stores each day. By combining room temperature sensor data and heat meter data we will do the same for a 

care home, a new heat network, and an apartment building on an existing heat network in London. We will 

develop metrics for Quality of Service - what the network delivered vs what the consumer requested - and 

predictive models that allow operators to run more of the network, cooler, for longer, without compromising 

the user experience. 

 Innovation: securely reading heat meters at high frequency and post-processing this this data to quantify 

&amp;quot;Quality of Service&amp;quot; - not just quantity of service - for heat networks in the UK and throughout Europe.</ns2:abstractText></ns2:project>