Keynote, Session 3
“Using data to build the market for low carbon renovation in buildings: the evolving data-driven services of energy agencies in providing publicly-funded advice on energetic renovation of buildings”
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David Weatherall, Head of Policy at the Energy Saving Trust, UK.
1. Using data to drive energy renovation of
homes: the Energy Agency Experience
A case study of Scotland
David Weatherall
Head of Poilicy
Energy Saving Trust
London, UK
2. About the
Energy Saving
Trust
• Energy Saving Trust is an independent
body established by UK government to
promote sustainable energy.
• Helping build the market for home
energy efficiency has always been at the
heart of our work, though today we also
work extensively in community and
renewable energy and in clean transport.
• We work with all the UK national
administrations – UK/England, Wales,
Scotland, and Northern Ireland – and on
many cross-Europe projects.
3. Proposed Trajectory for Refurbishment of Scotland’s
Homes
Energy Efficient Scotland Routemap Consultation, Scottish
Government 2018 https://www.gov.scot/publications/energy-
efficient-scotland-route-map/
4. Key aspects of domestic building energy
labelling regime in Scotland
• Scotland has own labelling system,
but shares regulatory and
technical infrastructure across UK;
• The headline A-G rating compares
energy cost by floor area based on a
1-100 scale;
• EPCs are issued individually for the
property (including apartments);
• Cost of £35-50 (€40-€55 approx.)
• EPC database managed by the
Energy Saving Trust;
Front page of the Scottish
EPC
5. Energy Saving Trust helping deliver energy
efficiency for Scotland’s homes
• EST delivers most of the government support
programmes for energy efficiency.
• Approx 100,000 household contacts each year,
33% on low incomes.
• Householder facing services include:
• Advice on potential home energy improvements based on
EPCs and other information, tailored to the customer
• Signposting/referral to available financing local support and loans or grant offers
• Benefit checks and identification of fuel poverty
• In-home support for vulnerable customers
• Referrals to support with energy tariffs.
• Guidance when installing renewable technologies,
including some in-home visits
• Integration of transport advise
• We also deliver capacity building support to local
authorities, district heating progs, community energy
groups and the supply chain to help build the
market for energy efficiency
6. Tackling climate change and fuel poverty
by promoting home energy efficiency
• General distinction in policies and therefore our work
between “able to pay” and “fuel poor” households
• In Scotland approx. 1 in 4 households in fuel poverty (paying
>10% of income on heat and light).
• Some of the main challenges to our work, that we are
currently using data-driven approaches to solve are:
Challenges
Fuel poor The difficulty in finding people who need help
• People don’t know they are fuel poor
• Often vulnerable – unlikely to come forward
Able to pay • Energy efficiency – not a priority
• Confusion over the costs and benefits of different actions
• How do I do it? Taking the next step?
9. The Issue
Targeting programmes – identifying what homes need what
measures is vital to enable programmes to be targeted
effectively, reducing the costs of finding households eligibile
for support. This is particularly a challenge as:
• We complete more of the cost effective measures in
homes
• With extensive fuel poverty programmes, we also need
to find the right homes with the right (ie low income)
households
10. Home Analytics
Home Analytics is a database that provides accurate
data for every domestic property in Scotland,
England and Wales.
Some of this data is obtained from multiple sources (e.g.,
EPC, HEED, HEC), while the rest is modeled using
statistical and geo-spatial modeling techniques.
Home Analytics has been used for:
• identifying homes at risk of fuel poverty
• targeting energy efficiency schemes
• adding context and additional value to other data sets
• reducing data inputs required from customers.
11. Variables in Home Analytics
11
• Wall, loft and floor insulation
• Primary fuel type
• Secondary fuel type
• Boiler type
• Main heating control
• Meter type
• Glazing type
• SAP fuel bill
• SAP energy rating and consumption
• SAP CO2 emissions
• EPC record present
• Presence of solar PV / thermal
• Suitability for:
• Solar PV / thermal
• Heat pumps (air source, ground)
• Biomass boilers
• Wind turbines
• Solar PV potential (kWh/kWp pa)
• Roof orientation
• Wind speed
• Distance to gas grid
• Index of Multiple Deprivation (e.g.
income, employment, education,
crime, health, etc.)
• Benefit claimant counts
• Risk of fuel poverty (LIHC definition)
• Risk of fuel poverty (10% definition)
• Risk of HHSRS category 1 excess cold
• Population in poor health
• Population in limited mobility
• Property type
• Property age
• Property tenure
• Property floor area
• Number of habitable rooms
• Building height
• Wall and floor type
• Presence of room in roof
• Number of dwellings in building
• Listed building grade
• Exposure zone
Building
Characteristics
Energy Efficiency
Measures
Vulnerability
Information
Renewable Energy
Suitability
12. Model accuracy summary
Modelled Variable Accuracy Accuracy to Within 1 Band
Property type 87% 89%
Property age 79% 89%
Property tenure 94% n/a
Habitable rooms 76% 92%
Primary fuel type 98% n/a
Secondary heating system 88% n/a
Secondary heating fuel type 87% n/a
Boiler type 78% n/a
Heating controls 93% n/a
Meter type 96% n/a
Wall construction 95% n/a
Wall insulation 95% n/a
Floor construction 89% n/a
Floor insulation 98% n/a
Loft insulation 84% 91%
Room in roof 99% n/a
Glazing type 100% n/a
Low energy lighting 71% 85%
SAP energy rating (EPC) 1 86% 98%
CO2 emissions 88% 98%
Home Analytics England & Wales v3
14. Developments from Home Analytics
Data
• PEAT tool – optimisation tool, uses Home
Analytics Data to create target driven
scenarios for stock improvement based
on budget and/or retrofit standard (eg
EPC “C”)
• Health Referral tool (flag to alert doctors
to residents potentially living in cold
conditions, integrated in existing patient
management systems)
15. Benefits and challenges for this
approach
Challenges
• Accessing available data
(costs of private data
sources)
• Local authority (municipality)
users can lack skills to
maximise data – ideally
needs consultancy support
alongside the provision of
the data
• Individual address level data
may not be accurate enough
for house-by-house
targeting
• Who has access at what
resolution? (eg supply
chain?)
Benefits
• Very powerful tool for
planning retrofit strategies,
at local, regional and
national level
• Data can be provided at
different resolutions for
different users – protects
privacy
• Maximises value of EPC data
by integrating with other
datasets.
17. The Issue
Energy Advice needs to be tailored to customers individual
homes and circumstances.
18. 1) The evolution of “Home Energy
Checks”
A home energy check provides a remote assessment of the home’s energy
performance, without the need for an in-home assessment.
Widely used, by EST and other similar agencies, to provide a picture of the
cost and direct energy saving benefits of different energy saving measures:
• As a self-completion online service by households
• And – where households don’t have an EPC - also used by telephone
advisors to get a snapshot of home energy performance, and costs of
benefits of action, as they’re talking to callers
19. How UK Home Energy Checks work
Take assumptions from RD-SAP
methodology used in production of EPCs
and:
• Develop a broader range of default
inputs (eg room size assumed on
property type and number of bedrooms)
• adjust standardised occupancy
assumptions to reflect real living
patterns.
• Allows input of energy bill data, where
available
• Often, to make the results more useful,
will ask about whether households
qualify for grants (based on whether
they are receiving welfare benefits).
20. Benefits and challenges of Home
Energy Check tools
Benefits
• A hugely important tool to
provide tailored advice.
• Enables the incorporation of
consumer preferences
• Real energy use and occupancy
data (unlike the EPC)
• Enables the provision of clear
straightforward information in
an easy to use format
Challenges
• Difficult balance between
amount of information
collected (and drop out rates),
and the accuracy of the results
• Householders’ knowledge of
their home and its systems may
be limited
• Narrow focus on costs and
financial savings, hard to
consider wider benefits.
• Needs to be integrated with
support to take the next steps
23. The Issue
Smart meters are being rolled out in UK homes.
They enable the sharing of information about real energy use
data, where customers permit this, and therefore the
provision of much more accurate advice and guidance about
the potential for energy efficiency, as well as helping
households to manage energy better.
24. Image from Which.co.uk
• The smart meter roll-out in the UK is due to complete by 2020 (but
will very likely be delayed)
• Customer’s half-hourly gas and electricity use data is transferred to
the central Data Communications Company
• With customer consent, third party providers can access the data
• Energy Saving Trust is building the capacity to access this data to
improve our advice delivery, based on a pilot programme
• To reinforce the data provided from EPCs and through Home Energy
Check tools, the use of smart meter data will enable real energy use
values to replace estimates.
Smart Meter Based Advice
25. “We have been able
to match it [energy
consumption] more
accurately to house
occupancy and set
timers more
accurately to reflect
when we are in the
property, when we
will be leaving
(switch off heating
at least 30 mins
before) etc.”
(Householder)
The Pilot Project
26. Image from Which.co.uk
The Service
• Report to households on patterns of energy use over days/weeks/months
• Advice on behavioural changes (eg switch of heating)
• Report on potential for energy saving measures with savings data based on real
energy use patterns
• Real energy cost data will come through DCC this will include new Time of Use
tariffs
• Also incorporates real external temperature data
The Process
• Customer gives consent to access data from DCC – data includes both meter
readings and information on current tariff, and provides occupancy information.
• Up to 72 hour period for data access
• Customer can receive a one-off email report or sign up for monthly reports
• EST working with universities to develop (as with the pilot) an online tool version
How it will work
28. Benefits and challenges of the Smart
Meter Advice Portal
Benefits
• Enables much more accurate
provision of advice
• Maximises the value of the
Smart Meter data
• Links real energy use patterns
not only to behaviour change
messages but also messages
about potential for retrofit
measures.
• Clear costs data very important
as smart meters enable more
sophisticated time of use
tariffs.
Challenges
• Delays in the roll out
• Cumbersome consents process
• How will we explain EPC data
and the smart meter data
• Could be superseded through
new second-by-second CAD
based services?
30. The Issue
• Many funding programmes in the UK are only available to
people at risk of fuel porvety.
• Households don’t know they are in fuel poverty, and may
be reluctant to identify themselves.
• Retrofit companies asking householders to prove they are
on low incomes is intrusive, difficult and does not respect
customers privacy.
• Retrofit programme managers need to verify ownership of
properties.
31. Datamatching to identify households at
risk of fuel poverty
• The UK’s national energy supplier obligation (ECO) programme is now entirely
focused on households at risk of fuel poverty
• “At risk of fuel poverty” is defined as receiving one or more of several qualifying
welfare payments, eg:
• Income Support (for people on low incomes)
• Jobseekers Allowance (for people out of work)
• Carer’s Allowance
• For companies delivering that programme on behalf of the energy suppliers,
EST delivers a datamatching service working with the UK Department for Work
and Pensions (DWP) This replaces the previous approach where companies
collected evidence in the form of letters directly from households.
• On identification of a group of potentially qualifying households, batch data is
transferred from the company via EST to be checked by DWP who will flag
(yes/no) whether the household is on any of the qualifying benefits
• As a related service we can now also perform home ownership checks for ECO
providers with the Land Register (Cadastral Register).
32. Benefits and challenges for this
approach
Benefits
• Makes easier for households
to benefit from the scheme
• Major improvement in
protecting customers’
privacy (GDPR compliance
etc)
• Rapid and cost-effective
Challenges
• Are qualifying benefits the
best way to identify the fuel
poor?
• Some data not available
from DWP
34. The Issue
Only 1 in 50 homes in Scotland currently have a renewable
energy system. Strong potential and need – significant off-
gas population and ambitious carbon targets.
Customers considering renewable heat and other deep
retrofit measures need impartial, expert advice beyond what
the EPC can provide. That’s required both to choose the right
measures for them and to understand costs and the next
steps in installation.
35. Helping Scottish households choose
renewables
New Developments
Energy storage
grants
Transport advice
offered during home
visits
District heating
advice
•Continued
promotion at home
buyers
•Events focused on
tenement residents
•Trialling Skypeto
deliver advice instead
of over the phone
36. Home Renewables Expert Advice
and Audit
• Expert advisors (trained in EPC production
but also specialist renewables expertise) will
complete an in-home visit
• Based on the EPC process
• Collect occupancy and real energy use
data (so not an asset rating process)
• Detailed modelling of different scenarios
for renewable energy (as well as
conventional systems) and discuss
consumer preferences
• Detailed guidance on next steps
37.
38. Benefits and Challenges of this
approach
Benefits
• Essential advice to build an
early market for home
renewables in Scotland.
• Helps build a specialist
energy audit industry.
Challenges
• Cost!
40. Assessment to support Minimum
Standards Regulations in Scotland
• “For some owner occupiers or landlords from the private rented
sector, the process from assessment through to improvement, to
meeting the Long-Term Domestic Standard will be straightforward
and the current EPC assessment should be able to be used. However,
there will be situations where the owner of the property may be
unsure of what measures to install, or want to know what to do to
achieve a higher standard. There are also technical issues around
buildings that could mean that some additional assessment will be
needed.”*
• A short life working group is currently supporting the Scottish
government to develop an enhanced assessment process to
support minimum standards
*Energy Efficient Scotland Consultation: Making our homes and buildings warmer,
greener and more efficient, Scottish Government, May 2018, Available at:
https://bit.ly/2FGq1NJ p.16
41. X-tendo -
eXTENDing the energy performance
assessment and certification schemes via a
mOdular approach
Feature 1: Smart readiness indicator
(SRI)
Feature 2: Comfort indicator
Feature 3: Outdoor air pollution
indicator
Feature 4: Integrating real energy
consumption data
Feature 5: Interaction with district
energy systems
Feature 6: EPC databases for
improved data-mining, quality and
compliance control
Feature 7: Building logbooks
Feature 8: Tailored
Recommendations
Feature 9: EPCs and Finance options
Feature 10: One Stop Shops
Xtendo is a planned Horizon 2020 project covering ten countries,
approved for funding in January 2019. The project will develop and
test new features for EPCs
42. Challenges & Issues for Energy Agencies
in delivering new data-driven
approaches
• Resourcing of energy agencies work – investment costs (Brexit…)
• Innovation culture
• Tend to be dwelling led approaches to analysis
• Apartments – 40% of Scottish stock – all approaches ive described at
dwelling level
• District level approaches to heat decarbonisation
• Probably broader engagement needed to work alongside these
services
• A lot will depend on whether Scotland holds its nerve on new
regulatory approaches.
Thank Leandro and the organiser
Head of Policy talking from a policy and delivery perspective about uses of data.
Talk through slides
Going to talk particularly about Scotland where EST plays lead role in delivery
Focus on HOW data driven services support delivery including benefits and challenges.
Not presented as best practice, it’s a set of information about where we are up to…. Very happy for you to tell me how we could be using different tools and services
, 5m people in Scotland 2.5m homes,
emphasise scale of ambition in Scotland – most radical aspect long term domestic standard, but its with voluntary action leading to mandatory
Scotland highly ambitious action to drive energetic renovation in homes – eg spending 4x as much as England
80%/100% carbon target
fuel poverty 25%
Note challenge is primarily existing homes
Refer to health benefits. Infrasturcutre investment 1/2bn over 3 years.
BACKGROUND: Scottish system
Around 50% of Scottish homes have EPCs
EST manages register.
Discuss uptake and interest in epcs
Explain SAP/RD-SAP:
Assessment process is based on our national calculation methodology for energy performance used in building regulations, known as SAP
For existing homes, reduced data SAP is used, where to allow a rapid and non-intrusive energy audit by allowing default values are used for example for U-values based on assumed insulation levels assessed on property age and brick patterns.
V cheap by European standards – that’s part of the design of the assessment process – but obviously low price has implications for data quality in the broad sense.
Right at the
General distinction point
Able to pay policies: loans and advice and support – also trialing some innovative products
Fuel poverty – grants and area based schemes
Coming regulation focused on different tenures.
Probably don’t through talk through this…
Emphasise EPC component
Modelled energy use, asset rating
This is accuracy where we can’t use direct EPC data. Prob skip over this.
Explain principally used by local authorities in Scotland – made available to them –
HEEPS ABS (Home Energy Efficiency Programme Scotland Area Based Scheme) follows an area-based approach with initial focus on the most deprived areas.
Local authorities responsible for delivery, who are considered best placed through their Local Housing Strategies to understand the nature of housing provision and to co-ordinate a local supply-chain.
Measures available are dependent on the schemes developed by each local authority and are free to the householder and open to all tenures.
PEAT is a tool which primarily helps Local Authorities to understand the costs and external funding available when investing in
retrofit programmes.
Similar to DREEAM presented yesterday, but may not be as sophisticated on the financial side.
It allows them to quickly asses the costs and benefits of various retrofit options for inclusion in their HEEPS: ABS
or SEEP submissions (funding programmes available from Scottish Government).
The impact that HEEPS:ABS investment would have on fuel bills, fuel poverty and CO2 emissions in the areas identified for inclusion can also be determined.
Lots of uses beyond that: social housing eg dreeam
Describe the postal process.
Occupancy and energy bill data – we know that we can up to 3x variation in how much energy different households will use living in properties with the same energy performance.
Note that you still have to collect the occupancy data. Note the challenge of EPC data not being right – may be offputting….
Notes as per slides
Pilot programme 2014-2017
Notes: Significant discussion between advisor and customer covering aspects of consent (eg right to withdraw)
production testing due to start this month
February
Discuss the limitations of the po
Note heat transition
Discuss limitations on heat recommendatons
Note real energy data and tailored recommendations build from SMAP and from in-home advisor tool
Not good at innovation (timeline of H2020 projects) – staff tend to be project managers
Funding constraints