improving building energy performance: the uk …...• electricity • gas • building...

OFFICIAL

IMPROVING BUILDINGS’ ENERGY PERFORMANCE: THE UK GOVERNMENT’S TOOLS AND POLICIES TO

ADDRESS THE CHALLENGES

Chris Nicholls

[email protected]

24th April 2018

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Overview

The evolution of understanding buildings’ energy use

Understanding domestic sector buildings

Domestic sector policies

Understanding non-domestic sector buildings

Non-domestic sector policies

Future development plans

Improving Buildings' Energy Performance: The UK Government's Tools and Policies2

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The Evolution of Understanding Buildings’ Energy Use

High level, survey and theoretical energy use

(Pre-2010)

Building-level energy use

(Post 2010)

Smart

(Starting now)

• DUKES (Digest of UK Energy Statistics)

• EC UK (Energy Consumption UK)

• Physics-based models

• Pros: Accurate, high-level energy use

• Cons: Little insight in how energy is actually used

•NEED (National Energy Efficiency Data framework)

•Building-level metered energy use data matched with buildings’ and occupants’ information and government scheme participation

•Pros: Actual energy use more accurately understood

• Insight into changes in energy use and abatement opportunities

•Cons: Relies on meter readings, some are estimated

•Non-domestic buildings matching issues

•Smart Meters

•½ hourly records of energy use

•Pros: fully accurate energy use data

•Better understanding of energy use switching/grid balancing

•Better understanding of appliances’ energy use•Cons: Current coverage is patchy


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The Gaps

• Understanding the HOW, WHY and WHAT behind energy use statistics

• How is energy used by households/organisations?

• How energy use varies across different households/organisations and why?

• What makes households/organisations change their energy use?

• What is the actual (not theoretical) impact of installing energy efficiency measures?

• What is the impact of government policies on the housing/non-domestic stock?

• And many more…


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Domestic NEED (National Energy Efficiency Data framework)

• Buildings’ meter point readings

• Electricity

• Gas

• Building characteristics (Valuation Office Agency)

• Property type (e.g. detached, terrace, flats etc.)

• Size and physical characteristics

• Age

• Household characteristics (Experian - modelled using their credit rating/consumer insight work)

• Income

• Tenure

• Participation in government schemes

• Energy Company Obligation (ECO)

• Green Deal

• Carbon Emissions Reduction Target (CERT)

• Community Energy Saving Programme (CESP)

• Gas safe boiler installations

• Energy Performance Certificate (EPC) rating


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Initial Insights

• How much energy do households really use?

• How much energy is saved by installing energy efficiency measures?

• How much energy could be saved by installing more energy efficiency measures?


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NEED – Gas Over Time


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Impact of Measures Methodology

• Difference in differences approach

• Each property in the intervention group is also matched to an equivalent property in the comparator group to allow comparisons of median, and distributions


InterventionPre-intervention

Post-intervention

Gas consumption

Time

A B

C

Intervention groupComparator group

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NEED – Impact of Energy Efficiency Measures


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Domestic Sector Policies

• To help meet the UK’s Carbon Budgets

• Energy Company Obligation (ECO)

• Warm Home Discount (WHD)

• Private Rental Sector regulations (EPC band E)

• Future, more stretching policies needed to meet Clean Growth Strategy’s 5th Carbon Budget’s aims


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Policies’ Common Features

• Rely on accurate understanding of abatement potential

• Rely on good understanding of costs and benefits

• Rely on identifying and persuading households/landlords to install measures


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NEED’s Impact on Abatement Potential


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NEED’s Impact on Costs and Benefits

• Most costs determined by other evidence, e.g. surveys/calls for evidence on installation/hidden costs

• Benefits:

• Energy/CO2 savings

• But if we decarbonise the fuels, benefits from CO2 savings drop off; benefits focussed on energy savings

• Comfort taking benefits

• Health benefits

• Quantifying the extent of under-heating

• Directly linking vulnerable households to health service data

• Enable more fine tuning of policies aimed at alleviating the worst fuel poverty


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NEED’s Impact on Behavioural Insight and Targeting

• Planning to build household typology in terms of their likelihood of participating in government insulation schemes

• Provide insight into alternative policies to persuade different household types

• Machine learning

• Better targeting of fuel poor households


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Further policy applications

• Green Finance Taskforce “Accelerating Green Finance”:

• Green mortgages

• Understanding borrowers’ affordability and lenders’ risk profile

• Improving the quality of EPC ratings

• Understanding better the implications of pushing higher energy efficiency standards

• How to get there

• Impact on energy use if we do


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Understanding of Non-Domestic Sector is Less Well Developed

• Issues:

• Multiple premises in buildings

• Use of premises differs

• Business models affect energy use

• High tenancy turnover

• Previous evidence base:

• N-DEEM (Non-Domestic Energy Efficiency Model)

• Used site-based surveys aggregated to nationwide using sampling frame

• Pros:

• Accurate record of specific organisation’s energy use and abatement potential

• Cons:

• Weak at explaining variation in energy use and abatement

• Didn’t link to N-D NEED, so missing opportunities for further insight


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Non-Domestic NEED


• Premises-level energy use

• Experian data on organisation occupying

premises

• Energy efficiency: EPC (Energy

Performance Certificate)

• Energy use rating: DEC (Display Energy

Certificate)

• Address matching low for non-domestic

NEED (c. 50% - accuracy rating gives a

probability that the property has been

matched to the meter correctly.)

• Energy use based on meter readings,

many of which are estimated.

• No installation record equivalent of

domestic NEED’s

FRAMEWORK

Coverage: England &

Wales

Consumption:

Metered electricity &

gas only

Non domestic

ratings spine:

Aggregated to

property/site level

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N-D NEED’s Aspirations

• To understand:

• How energy is used by organisations and sectors

• How energy use varies across different organisations and sectors

• What makes organisations change their energy use

• The actual (not theoretical) impact of installing energy efficiency measures

• The impact of government policies on the non-domestic stock

• But

• Poor premises matching and noise in data restricts the insight

• Little insight into what uses energy

• Little insight into abatement opportunities

• No linked database of installation of energy efficiency measures

• Solution:

• BEES (Building Energy Efficiency Survey)


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BEES’s Aims

• To update understanding of how energy is used across the non-domestic building stock in England and Wales

• To update understanding of how energy use can be reduced across the non-domestic building stock

• To understand the barriers and facilitators of energy abatement

• To capitalise on insights from N-D NEED


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BEES’s scope – 38 sub-sectorsSector Sub-sector Sector Sub-sector

Retail Hairdressers and beauty salons Health (cont.) Hospitals

Large food shops Nursing homes

Large non-food shops Education Nurseries

Showrooms Primary schools

Small shops Secondary schools

Retail warehouses Higher education – teaching & research

Office Private sector offices Higher education – residential

Public sector offices Emergency services Fire/Ambulance stations

Hospitality Pubs Law courts

Hotels Police Stations

Restaurants & takeaways Prisons

Cafes Military Military offices

Industrial Factories Military storage

Workshops Military accommodation

Storage Cold stores Community, arts & leisure Clubs & community centres

Large distribution warehouses Places of worship

Stores Museums, art galleries & libraries

Warehouses Theatres, concert halls & cinemas

Health Health centres Leisure centres


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BEES’s Methodology

• Building stock broken into 38 sub-sectors

• Sampling and grossing based on ND-NEED (which covers 89% of ND floorspace)

• 3,690 telephone surveys (approximately 25 mins)

• 214 site surveys (sample of telephone surveys)

• Tested/moderated the energy use and abatement models

• Verified/corrected telephone survey returns

• Alternative methods where telephone/site surveys couldn’t be conducted

• Barriers/enablers: 126 face to face interviews

• Removed outliers/don’t knows

• Energy use model: used survey evidence combined with other sources

• Abatement model: used survey evidence and database of energy efficiency measures


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BEES Energy Use Model – Tree Diagram


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BEES Energy Use Model Structure


• Model used to estimate energy consumption by 22 energy end uses (e.g. heating, lighting, small power, etc.) for a typical building or premises for a specific sub-sector, using reference sources. Supplemented by BEES survey, site audits and sub-sector experts.

• This typical building energy use model was then tailored to the actual building (premises) in each individual record using the information from the telephone survey for that building (premises).

• DEC data, site surveys and ND-NEED used to validate estimates

• UCL peer review

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BEES Abatement Model

• An energy end-use prediction for each premises, generated by the energy use model.

• Telephone survey responses on the premises.

• A database of energy efficiency measures, with defined energy saving impacts and cost factors, drawn from a range of sources.

• Model calibrated by: expert review; site survey comparison; and telephone survey mapping.


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BEES Insight

• Total energy use: 161,060 GWh/year

• Main uses:

• Space heating (66,940 GWh/year)• Internal lighting (21,260 GWh/year)

• Catering (13,270 GWh/year)

• Cooled storage (for storage of food and drink) (10,790 GWh/year)

• Abatement potential of 63,160 GWh/year (or 14,630 ktCO2e/year)

• 39 per cent reduction from current energy consumption• Over a third of the potential (22,080 GWh/year) came from measures with a private investment payback of <3 years

• The bill savings from measures with a private payback of <3 years was £1.3bn a year

• Largest abatement potential (representing 55 per cent of the total abatement potential)• Carbon & energy management,

• lighting replacement & control and

• building services instrumentation & control measures

• Barriers to energy efficiency:

• Economic (e.g. low capital availability, investment costs and interventions not sufficiently profitable); • Organisational barriers (e.g. complex decision chains, divergent interests);

• Barriers related to competencies (e.g. identifying the inefficiencies, implementing the interventions); and,

• Behavioural barriers (e.g. lack of interest in energy efficiency, inertia).


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BEES vs. ND-NEED: Electricity Consumption


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How BEES and ND-NEED have Influenced Policies

• Clean Growth Strategy – reducing business and public sector energy demand by 20% by 2032:

• Help set the target

• Helped understand how the target can be met

• Couldn’t have set the target without it

• ND-NEED helps us understand:

• how different policies interact

• which organisations are in scope for different policies

• E.g. simplified carbon reporting

• Impact of policies:

• Econometric analysis compared energy consumption between Carbon Reduction Commitment (CRC) participants and the comparison groups for the period 2006 to 2012. Findings: CRC participants reduced electricity consumption (>6,000MWh) by an annual average of 3-5% between 2010 and 2012, compared to information declarers (>2,000MWh, <6,000MWh).


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Other Value Added by BEES and ND-NEED

• Informing evaluation of:

• CRC (Carbon Reduction Commitment)

• CCA (Climate Change Agreements)

• ESOS (Energy Savings Opportunity Scheme)

• Public sector loans

• EDR (Electricity Demand Reduction)

• Stakeholders:

• We understand sector lobbying better, esp. for smaller organisations

• Better understanding of how abatement potential differs for different sectors


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Future Work

• Data science:

• Understanding outliers

• Benchmarking (esp. industries)

• Bills and operational costs - who's most exposed to energy costs within sectors

• Matching Non-Dom EPC data to NEED to see how good EPCs are

• Only 2/3rds of measures BEES recommends would be picked up by EPCs - missing: behaviour and controls

• Green Finance Taskforce’s recommendations

• Two big evidence gaps

• Heat - processes, heating and cooling

• Re-working BEES data to get additional insight into this

• What buildings currently use

• What would a good low carbon system look like?

• Industrial process energy efficiency

• Outside scope of BEES

• Very heterogeneous sector


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Challenges

Further Insight

References

Annexes

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Establishing the data-framework

• Unique address and property identifiers are key to NEED’s operation – the spine for all the other data sets

• Custom coding required to write the algorithm to process property identifier records

• Once unique property identifiers established (easier for domestic then non-domestic) the data-framework could be built

• Large database requires a shared SAS server across DECC

• NEED team:

• Establishing NEED required around 7 people, plus ad-hoc assistance from others around the department

• Challenge to adequately resource NEED on ongoing basis


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Challenges along the way

• Energy use based on meter readings, many of which are estimated.

• Address matching low for non-domestic NEED (c. 50% - accuracy rating gives a probability that the property has been matched to the meter correctly.)

• Non-domestic NEED does not have an installation record equivalent of domestic NEED’s.

• Measure installation is binary: there is no record of the percentage of a house covered with insulation.

• We have no record of DIY installations of measures

• Experian data for domestic sector (providing household characteristics) is modelled, so not wholly accurate

• Large database issues: data imports sometimes fail; run time can be slow

• Getting permissions from various parties


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Challenges

Further Insight

References

Annexes

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NEED – Distribution of energy use


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NEED – Type of Property


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NEED – Property Size


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NEED – Income


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NEED – Impact of Energy Efficiency Measures


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Energy Use When Households Move

Annual energy usage in year before and after change of occupancy in 2012 (KWh)

• NEED matched with Land Registry residential property sales data

• Sample excludes properties with known installations

• New buyer effect possibly due to buying new appliances, building modifications, behavioural change

• Are the effects permanent?


Gas Electricity

2011 2013 Change 2011 2013 Change

Properties sold in 2012 Mean 16,300 14,200 -2,100 4,500 3,500 -1,000

Median 15,000 13,000 -1,000 3,800 3,000 -800

Comparison Group Mean 16,200 15,600 -600 4,500 4,200 -300

Median 15,000 14,400 -600 3,900 3,600 -300

All Properties Mean 14,100 13,500 -600 4,200 4,000 -200

Median 12,900 12,400 -500 3,400 3,300 -100

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Further Insights Planned (Domestic)

• Deeper data science analysis: e.g. understanding outliers better; benchmarking etc.

• A lot of the WHY questions remain unanswered

• Match to other datasets with addresses to provide further insights


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Challenges

Further Insight

References

Annexes

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References

• Energy Statistics: https://www.gov.uk/government/organisations/department-for-business-energy-and-industrial-strategy/about/statistics

• DUKES: https://www.gov.uk/government/statistics/digest-of-uk-energy-statistics-dukes-2017-main-report

• ECUK: https://www.gov.uk/government/collections/energy-consumption-in-the-uk

• Sub-National Energy Statistics: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/678653/Sub-national_electricity_and_gas_consumption_summary_report_2016.pdf

• Standard Assessment Procedure (SAP): https://www.bre.co.uk/sap2012/page.jsp?id=2759

• NEED: https://www.gov.uk/government/statistics/national-energy-efficiency-data-framework-need-report-summary-of-analysis-2017

• BEES Overarching Report: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/565748/BEES_overarching_report_FINAL.pdf

• BEES Technical Annex: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/566038/BEES_Technical_Annex_FINAL.pdf

• CRS Evaluation: https://www.gov.uk/government/publications/evaluation-of-the-crc-energy-efficiency-scheme

• Clean Growth Strategy: https://www.gov.uk/government/publications/clean-growth-strategy

• Energy Company Obligation: https://www.gov.uk/government/consultations/energy-company-obligation-eco3-2018-to-2022

• Green Finance Taskforce: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/695176/green-finance-taskforce-accelerating-green-finance.pdf


https://www.gov.uk/government/organisations/department-for-business-energy-and-industrial-strategy/about/statistics

https://www.gov.uk/government/statistics/digest-of-uk-energy-statistics-dukes-2017-main-report

https://www.gov.uk/government/collections/energy-consumption-in-the-uk

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/678653/Sub-national_electricity_and_gas_consumption_summary_report_2016.pdf

https://www.bre.co.uk/sap2012/page.jsp?id=2759

https://www.gov.uk/government/statistics/national-energy-efficiency-data-framework-need-report-summary-of-analysis-2017

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/565748/BEES_overarching_report_FINAL.pdf

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/566038/BEES_Technical_Annex_FINAL.pdf

https://www.gov.uk/government/publications/evaluation-of-the-crc-energy-efficiency-scheme

https://www.gov.uk/government/publications/clean-growth-strategy

https://www.gov.uk/government/consultations/energy-company-obligation-eco3-2018-to-2022

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/695176/green-finance-taskforce-accelerating-green-finance.pdf

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What is NEED?

Energy company billing systems

Xoserve [For 4 aggregators -

approx. 20m meters]

IGTs [other 6 aggregators -

app. 1m meters]

Data aggregators[31 organisations]

Meter Number (MPAN) and Address

Meter readings

Gemserv

Electricity

Gas

Matching via AddressBase

UPRNMeter Number (MPRN), address and

consumption

Supplier permissions SoTA

Consumption and meter number (MPAN)

Meter readings

HEED energy efficiency

measures installed

(managed by EST)

Energy Performance Certificates

Experian Residata

Energy suppliers

EST web-survey (self completion)

Housing Surveys

Data from trade associations (e.g. FENSA, Gas Safe)

Walker Martin

Measures delivered through Gov’t schemes

Construction and measures installed in homes

Measure installed, date of installation and address

NEED Analysis file at VOA

(inc. prop attributes data)

Matching to other data

Output Area Classification

National Statistics Postcode Directory

Experian consumer surveys

Experian household demographics and mosaic

Modelling

Measures delivered (excluding supply info)

Household level estimates for income and tenure

Meter point consumption data

Energy efficiency measures installed

Household characteristics

Data inputs

Natio

nal E

nerg

y Effi

cienc

y Da

ta-F

ram

ewor

k (NE

ED)

Data inputs

Assessment by accredited

assessor

Census 2001

ONS data

Valuation Office Agency Property Attributes DataProperty surveys

Voluntary questionnaires

Information from Local Authorities on planning consent.

Freely available information e.g. information about properties sold. Property

attributes

Linking via UARN/UPRN

lookup

Consumption data(used for sub-nat)

GROS

NISRA

Royal Mail

Ordnance Survey

Energy Company Obligation

(ECO) measures installed

Ofgem

Obligated energy suppliers

Matching via AddressBase

UPRN


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