Understanding Effects of Technology on Energy Use … Sooriyabandara.pdf · The project consortium...

Copyright © 2012 Toshiba Corporation. All rights reserved

Understanding Effects of Technology on Energy Use Behaviour

Dr. Mahesh Sooriyabandara

Associate Managing Director

Telecommunications Research Laboratory

Toshiba Research Europe Limited, Bristol, UK


Outline

• Why is it important to understand energy use

behaviour?

• Designing pilots for intervention studies:

3eHouses project as a case study

• Theory-based interventions

• Early results

• What does this means for HEMS & DR business


Consumers and Costly Pilots “The customer: Smart grid’s central stakeholder”

PriceWaterHouseCoopers 2010 Chicago Tribune 2011

Jackson Associates 2009 Keynote, IEEE ISGT 2011

“... The $63 million smart grid pilot program ... disappointing results that reinforce what [Rowe] already knows..”

“Lack of consumer-

focused research.”

“But targeted, strategic technology deployments can significantly increase cost-benefit ratios”


Demand Side Pilots: Some Questions

• Basic Question

• How do we design pilots to meet goals when faced with uncertainty?

• Specific questions :

• How to analysis the causal effects of technology on energy use

behaviour?

• Does the order of interventions matter?

• General Questions

• What types of additional services would work and to what extent?

• Can we change consumer behaviour through interventions?

• Products and Services

• What is the role of technical interventions in HEMS & DR Services?


Designing Pilots:

To better capture causal effects of technological interventions


Planning Pilots for Energy Use Behaviour study

• Case study: 3e-Houses Project goals

– “….. interested in exploring, defining and measuring the change

of behaviour of participants (and possibly the broader community)

as a result of the technical interventions introduced as part of the

project.”

– “Motivating factors are individual, internal drivers of behaviour.

These factors are awareness, knowledge, social influence,

attitude, perceived capabilities and intention.”


Problem: Defining a Methodology

• No clear method of how to measure/assess

these impacts (especially causal effects)

• Most studies look at aggregate correlations and

don’t have granular data

• Identify socio-technical relationships at the

design stage through theoretical underpinning

and simulations to maximise value.


Planning Interventions

• Likely to have some transient response to interventions

• The impact of interventions are expected to relate to

behavioural and demographic variables


Case Study: 3e-Houses Project Introduction


3e-Houses

EC CIP PSP project: with the goal of achieving energy savings by

feeding back energy usage information to participants

The project comprises of four pilot studies in Spain, UK and Germany.

The project consortium brings together energy suppliers, utilities,

technology companies housing associations and local government

departments across Europe.


Bristol Pilot The locations for the Bristol replicator are

60 flats in Fremantle house in Dove Street

...

...and around 40 houses in Knowle

West

• HEMS technology:

– sensor network to capture high frame rate energy usage data.

– A front end interface on a Tablet that presents the participants

with this information as apart of intervention study.


3e-Houses HEMS

Tablet Display

Smart Plug Smart Plug Smart Plug

Metering

HEMS HUB

HEMS

Cloud Server

Gas Meter Thermometer

Off-Peak

Electricity

Peak

Electricity

Lighting

Sensors within each house detect 11 independent electrical loads, gas

usage and indoor temperature at minute granularity.


Planning Interventions

Theory based Interventions backed by simulations


Building a Theory-based intervention study

Extension of Ajzen’s TPB – Theory of Planned behaviour

Literature shows that Ajzen’s TPB is related to changes in energy consumption


Methodology: Quantifying behaviours

Formalise

Framework

Represent each household as a control system


Simulation Results: Consumer Segmentation


Intervention Methodology:


Interventions Methodology

Pha

se 0

Metering

No interface

Pha

se 1

Tablet

Aggregated data

Pha

se 2

Energy Workshop

Pha

se 3

Neighbourhood average feedback

Pha

se 4

Energy Saving Tips

Disaggregated data

Questionnaire Questionnaire Questionnaire

Time

• Various interventions over a 1 year period.

• Measure consumption and intention (via TPB questionnaire).

• Understand impact of each method on households.


Community Engagement

Explaining the participant about the energy management system.

Community Workshops to introduce

the technology Participant with the Toshiba Tablet


Initial Results:

Intervention, efficiency and attribute relationship


Analysis

• Analysis of first consumption and intention

survey

• Efficiency analysis after first intervention; tablet

and feedback interface

• Relationship between efficiency, intervention and

attitude

• Ongoing - data collection and further analysis


Efficiency Analysis: Classification of homes

Low Impact

High Impact

Very Important

Less Important


Initial Participant Surveys

• Positive attitude: “eco friendly”

– Less influenced by their peers

– Feel they have a strong control

over their consumption.

• Negative attitude: “eco cautious”

– More easily influenced by their

peers

– Feel they have poor control over

their consumption.

Suggest behaviour splits according to energy conservation

attitudes:


Effect of consumption after first Intervention

• We have monitored each house before and after they see their consumption data.

• Some users have significant reductions in usage.

• Some have not changed.

• Some have increased.

Learn from users with

significant energy

improvements

Target users who can benefit most

Some users have not changed

We can analyse each

group separately to see

what other behavioural

changes have occurred.


• Plot shows the % reduction in non-

heating energy use.

• Those 19 homes in top half have

significant reductions more than 8%

– Split between both housing estates.

– Need to discover if these are still heavily

engaged with the project.

• Some show increase in energy use.

– Interesting to study more closely to see

what has happened.

Compare before and after tablet Intervention


Consumption Reduction vs Eco Friendly Attitude

• What is the correlation

between attitude and

reduction due to first

intervention?


Energy use behaviour:

Future energy services such as HEMS and DR


Demand Response case

• DR should be able to provide a reliable service

such that a SLA can be made with a

utility/network operator

• Research suggests that when utilizing direct load

control (DLC) and load shifting (LS) techniques

– Size of the participating population and consumption

flexibility (time and amount) matters


Willingness to Participate in DR

Ancillary service guarantee – how to deal with uncertainty of customer energy usage?

Higher

participation

values give more

frequent

rebounds

Very rare

rebounds; but

still do occur.

Higher average

magnitude.


Creating new energy service businesses?

• DR business models – flexibility of customer side

is important to realize certain type of DR services

• Interventions that would make customer change

their behaviour (i.e. accept a life style change)?

• Critical mass of participation/flexibility needed to

realise a reliable DR service


Summary

• Generalisation to pilot study design:

– How can we minimise risk in the face of uncertainty?

– Use of theoretical basis at the design stage to

maximise value.

• Energy use behaviour and new energy services • Will provide more directed insight on the influence of

technology on consumer behaviour

Understanding Effects of Technology on Energy Use … Sooriyabandara.pdf · The project consortium...

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