As part of my role in a UK Green Building Council Task Group developing incentives to encourage people to take up the Green Deal or other energy efficiency strategies I produced a report on how smart users might encourage energy efficient behaviour which is reproduced below:
UKGBC Green Deal Incentives EEFiT Sub-Task Group
Briefing Note:
Smart meters as part of an energy
efficient behaviour incentive scheme
Paul Appleby
Rev 2: December2013
Introduction
This note has been produced as a
result of discussions held by a working group considering one of a number of
alternative schemes to incentivise energy efficient retrofit. The EEFiT (Energy
Efficiency Feed-in Tariff) sub-task group was investigating potential financial
rewards based on either deemed or measured energy savings.
The aim of this offshoot has been
to investigate the potential for linking smart meters to an incentive scheme
that rewards reduction in energy consumption due to the actions taken by the
householder: whether through installation of energy saving measures or energy
saving behaviours.
A smart meter system installed in
a home as part of a supplier’s roll out obligation comprises an electricity
meter, a communications hub, a gas meter and an in-home display (IHD). It is
expected that over time a market for other devices that connect to the system
to access consumption and pricing information will develop. These devices have
a generic name “CAD” (consumer access device) and could come in a variety of
forms.
CADs may be integrated into a number
of devices, for example as part of a home network that can communicate with
thermostats, fridges etc. This could allow communication through broadband so
that third parties can access data.
The foundation stage of smart
meter roll-out is based around Smart Metering Equipment Technical Specification
#1 (SMETS1) which includes a CAD interface, but not the communications standard
for the interface. However the next generation (SMETS2) mandates the ZigBee communication
standard and will be rolled out in Britain from the end of 2015.
It is likely that the large
majority of SMETS1 installations will use an earlier version of the ZigBee
standard than that planned for SMETS2. It is also likely that IHDs and other
CADs will have some degree of interoperability.
Two out of the 6 big energy suppliers
are committed to early roll out of smart meters. British Gas and EON are the most
advanced. It is thought that over 1 million will be installed before the
beginning of mass roll out of SMETS2 meters. However it is understood that the
approximately 500,000 meters that British Gas has installed under Phase 2 of
its programme are not SMETS1 compatible, whilst its Phase 3 meters are SMETS1
capable using a remote firmware update.
A number of contracts have been
let to handle the development and operation of the communications
infrastructure for connecting smart meters to the energy suppliers and handling
the data. This includes a Data Communications Company (DCC), Data Services Provider
(DSP), Communications Service Providers (CSP) and Smart Energy Code
Administrator (SECA).
The DCC will provide data and
communication services for the whole of GB, with responsibility for managing
the link between meters and suppliers, as well as other authorised third
parties. It will have a mandate to attempt to connect to all smart meters. Time
has been allowed in the roll-out programme to allow for this and avoid the risk
of stranded assets. However data from SMETS1 meters not enrolled into DCC will
be managed by energy suppliers. The
DSP will develop and operate the system controlling the movement of messages to
and from smart meters, the CSPs will provide wide area communications to and
from the smart meters and the SECA will maintain and update the industry code
governing the use of smart meters across the energy industry.
Subject to consumer consent, data
will be available over the wide area network (WAN) including 13 months of half
hourly data.
Algorithms for analysing data
from smart meters would be run on central servers since SMETS compliant meters
would not have the computational means to implement them locally. The data from
all versions of SMETS compliant meters will be the same and hence can be run by
anyone who can get access to the data.
Discussion
It is interesting to note that
Guy Newey of the UK Policy Exchange think tank in his report Smarter, Greener, Cheaper, dated January
2013, supports the introduction of an EEFIT but expresses some doubt on its
relationship to ‘behaviour change programmes’:
Measures to subsidise changes in
energy behaviour in the domestic sector through ECO should not preclude
introducing an energy efficiency feed-in tariff (EEFIT), to offer fixed
payments for verifiable reductions in electricity demand (as the Government is
consulting on). Such an instrument should allow electricity demand reduction
programmes to compete with new low carbon generation, potentially bringing down
the overall cost of meeting the UK's carbon targets and energy bills. However,
it is unlikely such a measure will support behaviour change programmes in
homes.
The following is extracted from a
Box towards the end of the report setting out the ‘Practicalities of proving
cuts in demand have taken place’:
One of the complexities of a
market that rewards demand reduction is proving that such reductions would not
have happened anyway, i.e. that the cuts in energy use are additional. A
reduction in energy use could be the result of several factors, unrelated to
any intervention. This could include warmer weather reducing the demand for
heating. Such a problem is not impossible to overcome and methodologies have
been successfuly used in the United States.
(Weather compensated controls have been used in commercial buildings
for many years of course, but have had limited success when applied to
residential applications, possibly due to the lack of experience amongst
installers and householders)
There are two broad options for making
comparisons:
1. Compare energy use for
individual households following the intervention to energy use (before) the intervention. This requires
suppliers (or whoever runs the programme) to establish a baseline. The data
would have to be normalised depending on weather.
2. Compare the energy use of
those households who get the intervention to a control group who do not. This
is the approach used by Opower in the United States. Households with similar
charactersitics are identified and those who do not have the intervention are
used to provide a benchmark (this allows for factors such as different weather
in different years to be less important). Any energy savings from those within
the group who have the intervention are then scored, and would (be) paid a subsidy.
Such approaches are not perfect
and may not account for changes such as different levels of occupancy. Robust
methodologies need to be developed before behavioural programmes can be
rolled-out nationally and allowed to fully compete with insulation measures for
subsidy, which is why the first step for Government should allow new pilots to
be subsidised through ECO. It is worth stressing that current support for
technical measures relies on assumed or 'nominal' savings rather than actual
savings. A comparison approach based on real energy (and therefore carbon)
savings helps tackle problems like the rebound effect.
Another problem is ensuring that
the changes people make in behaviour are sustained. While there is a reasonable
certainty that insulation will continue to work for several decades (although
no guarantee it will lead to overall reductions in energy use), there is a
concern that behavioural steps will 'wear off' and people will revert to old
habits.
Currently, there is some evidence
that changes in how people use energy are sustained over a period of more than
two years. Improved data collection, aided by smart meters, should make it
easier to monitor whether changes in behaviour have been sustained (although
checking will add extra cost). The subsidy could be paid for each year the
savings can be shown to continue compared to a baseline (or) benchmark.
In a recent study that pulled
together the results from ‘publicly available large-scale independent
evaluations of comparative energy use feedback programs in the United States
Agnew et al of energy consultants DNV KEMA concluded that:
·
Energy savings of between 1 and 3% resulted from
providing householders with comparison data with their neighbours’ energy use
·
Savings increased year-on-year and with the
frequency of reports
·
Householders found comparison reports useful
·
Savings were generally smaller than confidence
intervals.
The critical factor in any scheme
that rewards behaviour is the determination of the baseline or benchmark
referred to by Newey above. The approach by US-based Opower uses what it refers
to as ‘Big Data Analytics’4. It
claims that data is analysed from some 35 million smart meters and in-home
displays across the US to determine trends in energy consumption, determine benchmarks
and assess the success of energy efficiency programmes. However it is
understood that Opower’s platform is struggling with the volume of data
incoming, and it may be towards the end of 2014 before it has been upgraded to
handle this volume.
Without detailed information from
individual households it is not easy to determine what factors are impacting
energy use at any point in time.
Key factors that impact on energy
use that are not dependent upon behaviour or energy efficiency measures being
installed include weather, number and
type of occupants, number and type of appliances and change of use. Essentially
there are two methods of differentiating between behaviour/measure dependent
and other changes to energy profiles:
1.
Compare energy consumption against average for
homes with similar accommodation, normalised for local climate.
2.
Compare with previous year’s energy profile,
corrected for differences in weather and atypical changes in profile.
If, for example, a baby is born
into a family home then energy and water consumption are likely to rise quite
suddenly. Similarly if an occupant retires or starts working from home, hours
of occupancy will increase with correspondingly greater energy and water use.
These will result in steps in the energy profile which can be identified
through trend analysis in a similar manner to water leak detection.
In the research for this note the
author has spoken to a number of authorities who are involved with developing
the software and technologies for the smart meter roll out in the UK, as well
as civil servants and energy suppliers involved in the programme (see Annex).
None of those consulted were involved in developing systems that would provide
this trend analysis, although Onzo6 has developed IHDs that can
discriminate between different types of appliance through using data that is
collected at 1 second intervals.
Clearly DECC understands the
potential power of smart meters to collect information and augment energy
efficiency programmes. This is illustrated in the following extracts from
DECC’s consultation document for its Smart Metering Implementation Programme
Consumer engagement strategy dated April 2012:
There is some evidence from
supplier trials that customers with smart meters are more likely to take up
offers of energy efficiency products. There is also some evidence that
providing detailed feedback and information alongside ‘retrofit’ energy
efficiency programmes can significantly increase energy saving levels. With
smart metering, it will also be possible to interpret consumption patterns and
offer additional advice on how to reduce waste. Accurate feedback on savings
could also be valuable in increasing credibility and providing reassurance on
the achievement of projected savings under the Green Deal.
It also leaves the door open for
using smart meters as part of an energy efficiency incentive scheme:
In the medium term we will also
carry out further policy analysis to decide whether there is a positive case
for adjusting specific policy levers in order to provide additional incentives
for suppliers to help consumers save energy in ways enabled by smart metering.
Conclusions and Recommendations
It is the author’s view that there is scope for developing a
scheme that can be used to monitor and analyse data from individual smart
meters designed to distinguish energy saving behaviour from other factors that
might change energy consumption. An alternative scheme that compares energy
consumption against similar dwellings has already been piloted by Opower in the
United States. In the UK DECC should consider a pilot, perhaps in concert with
an energy provider such as British Gas, that is designed to compare these two
schemes. This pilot should address the following issues/questions:
1.
Can a methodology be developed that accurately
separates energy savings due to behaviour or installed measures from other
factors?
2.
The extent to which the rebound effect can
increase energy consumption
3.
Will year-on-year comparisons better promote
long term behaviour change than using a fixed benchmark?
4.
Can a scheme be developed that is accessible by
all, including those in fuel poverty?
5.
Does this scheme need to reward the installation
of energy saving measures and if so how can the resultant energy savings be
distinguished from other factors?
Annex: Notes of meetings and telephone conversations
1. Telecon with Peter Morgan, Smart Meters and
Fuel Poverty Team, DECC - 28/3/2013
Meters store 13 months of half hourly
consumption that can be accessed via a consumer access device via HAN or the
WAN. . In addition, real time
consumption and pricing data is available to the CAD over the HAN.
An authorised third party such as
an aggregator or ESCO will be able to access the data via the WAN ( as a Data
& Communications Company (DCC) service) subject to consumer consent. In addition, any party can enter into an
arrangement with a consumer (subject to consumer consent) to access data over
the HAN. This avoids complication of rewriting billing software – hence a
traditional trend analysis algorithm can be applied similar to that used to
identify major water leaks.
2. Telecon with Chris Shelley, Smart meter
consultant – 28/3/2013
DECC are keen that smart meters
be used to change behaviour.
Smart meter system in home
comprises electricity meter with integral comms hub (CAD) and gas meter which
talks to comms hub, and an in home display (IHD): all wireless.
The foundation stage of smart
meter roll-out is based around Smart Metering Equipment Technical Specification
#1 (SMETS1) which does includes and interface for a consumer access device
(CAD). CADs may be integrated into the IHD and establish a wireless home
network that can communicate with thermostats, fridges etc. This would allow
communication through broadband so that third parties can access data.
3. Meeting with Peter Morgan (PM) and
Charlotte Middleton (CM), DECC Smart Meter and Fuel Poverty Team – 14/5/2013
The potential for using smart meter
consumer access devices (CAD) as a hub for an energy efficiency incentive
scheme was discussed. This could use the energy demand profile or signature for
a home as a basis for comparing year-on-year changes in energy use based on
behaviour. It was agreed that there is potential to develop an algorithm that
adjusts the baseline profile for both step changes that might occur due to
change of use and atypical weather conditions – using either outdoor sensors or
Met Office data.
PM referred to an extensive programme
of work being carried out by British Gas (BG), including a survey of 18,000 of
its customers fitted with smart meters, who are providing regular feedback on
behaviour-related factors. BG has access to ring-fenced Warm Front money and
may be able to assist in assessing the feasibility of a measured incentive scheme.
PM also suggested that the Consumer
Energy Display Interest Group (CEDIG - a part of BEAMA) might be able to help
The key is being able to demonstrate that access by the vulnerable and fuel
poor will be improved by this incentive. It is possible that for many the very
fact of having a smart meter with an in-home display (IHD) will improve their
energy saving behaviour – for example a major international review of smart
meter programmes by VaasaETT in 2011 reported that across some 450,000 homes worldwide
IHDs resulted in an average reduction in energy use of 8.7% compared with
between 5 and 6% for detailed billing or information provided over the
internet, although whether this will lessen as the novelty wears off is
uncertain. The VaasaETT report concludes that where consumer engagement is
maintained reductions tend to continue over time.
PM agreed to supply contacts at the
Energy Services Trade Association (ESTA)
The roll-out of SMETS2 meters has been
delayed and will now occur between 2015 and 2020.
Based on DECC consumer research and
feedback from energy suppliers already deploying smart meters. CM suggested
that uptake of smart meters is likely to be very high. There is no Government
policy at present that will require those that refuse a smart meter to be
charged extra.*
CM also referred to Acorn profiling,
that uses census data and consumer profiles, but we didn’t elaborate on how
this might be used.
*In their response to a question
concerning this issue from the Energy & Climate Change Committee published
in their 7th report of the 2013/14 session published in October
2013, Ofgem stated that:
Our Retail Market Review reforms forbid
the levying of additional surcharges, such as a surcharge for opting out of
smart meters. However, as the roll-out progresses, the underlying costs of
serving traditional consumers may increase, due to reductions in economies of
scale and scope as the number of smart meters increase. Suppliers may look for
ways to recover these increased costs from consumers who have traditional
meters. Smart meters will also lead to efficiencies for suppliers and they may,
over time, choose to pass related savings on to consumers with smart meters
only.
Note: according to its own publicity ‘Acorn
is a powerful consumer classification that segments the UK population. By
analysing demographic data, social factors, population and consumer behaviour,
it provides precise information and an understanding of different types of
people. Acorn provides valuable consumer insight helping you target, acquire
and develop profitable customer relationships and improve service delivery.’ (http://acorn.caci.co.uk/)
4 Telecon with Simon Anderson, Chief Strategy
Officer at Green Energy Options – 24/05/2013
SA is a member of the IHD group of
CEDIG.
He suggested that consumers should be
rewarded with lower tariff for using less energy than typical for type of property.
Consumers should be bracketed within peer groups for comparison purposes.
Those working in this area include
Opower in US*, Tom Hargreaves at UEA.
*Opower has developed a customer
engagement platform which they call Big Data Analytics, which in the US has
involved storing and processing data from tens of millions of smart meters at
15-minute intervals, as well as second-level data from in-home devices (http://opower.com/products/big-data-analytics)
5 Telecon with Alastair Manson,
engage-consulting/Energy UK – 20/06/2013
AM is not aware of anyone having
developed a method of filtering out non-behaviour based changes in energy
consumption.
CAD has not yet been defined but IHD
has been specified to operate with 10 second updates. This interval is
independent of Broadband performance.
AM is working on Home Area Network
standard for appliances, taking into account conformity with ZigBee home
automation standards (http://www.zigbee.org/Standards/ZigBeeHomeAutomation/Overview.aspx) which is intended for use worldwide
but is US based.
6 Telecon with Nick Hunn, Onzo – 26/6/2013
Onzo has developed an IHD using funding
from SSE (Scottish & Southern Energy). He has been working on
disaggregation of energy consumption data using resolutions of down to 1 per
second, producing large amounts of data which are compressed at source. NH is
also looking at being able to identify energy saving behaviours.
NH reported that some $85bn is being
spent in the US on smart metering and energy education (TV advertising is 1/3
of this). Studies have indicated that this has resulted in 1-2% reduction in
energy condumption, although the contribution of smart meters has not been
determined.
He is worried that the CAD
specification being developed in UK is so complex that it will not be
compatible with anyone else in the world.
7 Telecon with Michael Harrison, Head of
Benefits & Evaluation, Smart Meter Implementation Programme, DECC – 27/6/2013
MH referred to the report ‘Smarter,
Greener, Cheaper’ by Guy Newey of Policy Exchange, which advocates sharing data
on energy consumption between similar dwellings. He also mentioned the Opower
big data analytics model (see above) and work being done by Cambridge
Architectural Research and Loughborough University for DECC analysing the results
of the 2012 Household Electricity Survey. (https://www.gov.uk/government/publications/early-findings-demand-side-management). Loughborough Uni are also involved
with a study measuring the impact of smart meters on energy consumption.
DECC has also published the most recent
analysis of the National Energy Efficiency Data (NEED) Framework (https://www.gov.uk/government/publications/national-energy-efficiency-data-framework-need-report-summary-of-analysis-2013-part-1)
Approximately 1 million smart meters
have been installed in UK (mostly by British Gas – see below)
8 Meeting with Jacqueline Mitchell, Head of
Energy Insight at Centrica/British Gas – 8/7/2013
BG has supplied around 500,000 smart
meters to their customers and are planning to offer a free Energy Report based
on smart meter data. This will provide both historical energy use and
benchmarking against similar homes, using an algorithm to provide an estimated
break down between different appliances (based on typical energy use). IHD uses
a 30 min interval. BG’s smart meters are upgradeable to SMETS1, but JM is
unsure whether they will upgrade to SMETS2. In later communications it was
gleaned that BG’s Phase 2 meters cannot be upgraded to SMETS1 or 2, whilst the
Phase 3 meters, which currently constitute 20% of its installed meters, will be
upgraded by a remote firmware update.
