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Journal ArticleDOI

Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior

01 Aug 2013-Vol. 101, Iss: 11, pp 2397-2408

TL;DR: A discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage are presented.
Abstract: Smart homes hold the potential for increasing energy efficiency, decreasing costs of energy use, decreasing the carbon footprint by including renewable resources, and transforming the role of the occupant. At the crux of the smart home is an efficient electric energy management system that is enabled by emerging technologies in the electricity grid and consumer electronics. This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage.
Topics: Smart grid (63%), Efficient energy use (57%), Energy management (56%), Energy conservation (54%), Load management (53%)

Content maybe subject to copyright    Report

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308
Electric Energy Management in
the Smart Home: Perspectives
on Enabling Technologies and
Consumer Behavior
Preprint
A
. Zipperer, P. A. Aloise-Young,
S
. Suryanarayanan, and D. Zimmerle
Colorado State University
R
. Roche
University of Technology of Belfort
-Montbeliard
L
. Earle and D. Christensen
National Renewable Energy Laboratory
P
. Bauleo
Fort Collins Utilities
To be p
ublished in Proceedings of the IEEE
Journal Article
NREL/JA-5500-57586
August 2013

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1
This report is available at no cost from the
National Renewable Energy Laboratory (NREL)
at www.nrel.gov/publications.
Abstract Smart homes hold the potential for increasing
energy efficiency, decreasing costs of energy use, decreasing the
carbon footprint by including renewable resources, and trans-
forming the role of the occupant. At the crux of the smart home
is an efficient electric energy management system that is enabled
by emerging technologies in the electricity grid and consumer
electronics. This article presents a discussion of the state-of-the-
art in electricity management in smart homes, the various
enabling technologies that will accelerate this concept, and topics
around consumer behavior with respect to energy usage.
Index Termsbehavioral science, consumer behavior, decision
making, energy management, load management, smart grid,
smart home
I. INTRODUCTION
smart home may be defined as a well-designed structure
with sufficient access to assets, communication, controls,
data, and information technologies for enhancing the occu-
pants’ quality of life through comfort, convenience, reduced
costs, and increased connectivity [1]. The idea has been
widely acknowledged for decades, but few people have ever
seen a smart home, and fewer still have occupied one. A
commonly cited reason for this slow growth has been the
exorbitant cost associated with upgrading existing building
stock to include “smart” technologies such as network
connected appliances [1]. However, consumers have histori-
cally been willing to incur significant costs for new communi-
This work was supported in part by: the U.S. Department of Energy under
Contract No. DE-AC36-08GO28308 with the National Renewable Energy
Laboratory; US National Science Foundation Award # 0931748; and the
Program of Research and Scholarly Excellence (PRSE) grant from the Office
of the Vice President for Research (VPR) at Colorado State University.
A. Zipperer, D. Zimmerle, P. Aloise-Young, and S. Suryanarayanan are
with Colorado State University in Fort Collins, CO, United States (email:
adam.zipperer@rams.colostate.edu).
R. Roche is with IRTES-SET at the University of Technology of Belfort-
Montbéliard in Belfort, France (email: robin.roche@utbm.fr).
L. Earle and D. Christensen are with the National Renewable Energy
Laboratory in Golden, CO, United States (email: dane.christensen@nrel.gov).
P. Bauleo is with Fort Collins Utilities in Fort Collins, CO, United States
(email: pbauleo@fcgov.com).
cation technologies, such as cellular telephones, broadband
internet connections, and television services. Table I presents
the changes in US mean monthly income by quintiles and
overall consumer spending on communication services,
adjusted for 2011 USD [2][6].
TABLE I. US MONTHLY INCOME AND COMMUNICATIONS
EXPENDITURES FOR 1980 AND 2008
This information may indicate that consumers are not
averse to significantly changing their spending habits with the
advent of new technologies. According to the US Bureau of
Labor Statistics the average homeowner spent approximately
11% more on entertainment (including cell phone and internet
services) in 2010 than 25 years ago [2]. Data indicate that
consumers are willing to spend more on hybrid vehicles than
on similarly sized traditional vehicles for reasons other than
economic payback [7].
Diverse motivations can lead to the same end goal. For
instance, a consumer may purchase a hybrid vehicle to
decrease carbon emissions, reduce dependency on foreign oil,
save money, or simply as a status symbol. Regardless, driving
a hybrid vehicle contributes to a more sustainable energy
future by reducing oil consumption and greenhouse gas
emissions. Similarly, marketing or societal influences may
motivate consumers to invest in smart home technologies.
The authors contend that cost and the lack of perceived
value have combined to slow the adoption of smart homes.
However, the perceived value of a smart home is likely to
vary across populations [8].
Electric Energy Management in the Smart
Home: Perspectives on Enabling Technologies
and Consumer Behavior
Adam Zipperer, Student Member, IEEE, Patricia A. Aloise-Young, Siddharth Suryanarayanan, Senior
Member, IEEE, Robin Roche, Member, IEEE, Lieko Earle, Member, IEEE, Dane Christensen,
Member, IEEE, Pablo Bauleo, Daniel Zimmerle, Member, IEEE
A
1980 ($) 2008 ($) Change (%)
Mean Income Quintile I 4,310 11,656 170
Mean Income Quintile II 10,727 29,517 175
Mean Income Quintile III 17,701 50,132 183
Mean Income Quintile IV 26,078 79,760 206
Mean Income Quintile V 46,497 171,057 268
Communication Services 22 117 432

2
This report is available at no cost from the
National Renewable Energy Laboratory (NREL)
at www.nrel.gov/publications.
The US Energy Information Administration (EIA) estimates
that 37% of end use electricity in the US is consumed in
residences [4]. Concomitantly, household appliances,
consumer electronics, and construction techniques are
becoming increasingly efficient [5]. As the Smart Grid
Initiative in the US progresses, the end-user is enabled with
near-real time information from the service provider [9]. This
presents an opportunity to coordinate the management of
appliances and other loads in the smart home, considering
information flow and end-user behavior.
This paper is organized as follows: section II describes a
smart home; section III outlines the assets and control
strategies in a smart home; section IV presents some enabling
technologies; section V explains consumer energy behavior,
especially in a residential environment; and, section VI offers
concluding remarks.
II. S
MART HOMES
A. What is a Smart Home?
A home is already a well-designed connector for power
transfer between the electricity grid and energy-consuming
appliances. A smart home also functions as a switchboard for
data flow among appliances and participants such as the end-
user, the electric utility, and a third party aggregator [9], [10].
This evolved capability benefits stakeholders on both sides of
the interface utility customers, utilities, and third party
energy management firms because there are strong incen-
tives for all sides to help the others function smoothly. For
instance, a homeowner may not inherently care about the peak
demand issues faced by the utility, but electricity prices and
supply reliability are tied to operational practices of the
service provider. On the other hand, a utility may be primarily
concerned with meeting the requirements of public utility
commissions, but unhappy ratepayers may result in business
and regulatory risks.
Looking outward, a smart residential building has two-way
communication with the utility grid, enabled by a smart meter,
shown in Fig. 1, so that it can interact dynamically with the
grid system, receiving signals from the service provider and
responding with information on usage and diagnostics. A
detailed description of the smart meter is provided in section
IV. This bidirectional information exchange is enabled by the
rapid adoption of advanced metering infrastructure (AMI).
Fig. 1. A smart meter at a residence. Photo sourced from National Renewable
Energy Laboratory (NREL) database, PIX 21394.
Looking inward, a smart home employs automated home
energy management (AHEM), an elegant network that self
manages end-use systems based on information flowing from
the occupants and the smart meter. The value of AHEM is in
reconciliation of the energy use of connected systems in a
house with the occupant’s objectives of comfort and cost as
well as the information received from the service provider.
Sensors and controls work together via a wireless home area
network (HAN) to gather relevant data [11], process the
information using effective algorithms, and implement control
strategies that simultaneously co-optimize several objectives:
comfort and convenience at minimal cost to the occupant,
efficiency in energy consumption, and timely response to the
request of the service provider [12]. An example of a smart
home, constructed in a laboratory setting at NREL, is shown
in Fig. 2.

3
This report is available at no cost from the
National Renewable Energy Laboratory (NREL)
at www.nrel.gov/publications.
Fig. 2. NREL’s AHEM laboratory. Photo sourced from NREL database, PIX
20207.
B. Economic feasibility and likelihood of widespread
adoption
Several market and technology trends are expected to
accelerate the development of cost-effective AHEM systems
that enable smart homes. These include:
Implementation of smart grids and continued growth in
home offices will expand market penetration of secure
HANs.
Growth in web-based cloud computing applications will
enable low-cost home energy data storage, data display,
and data analysis for AHEM trend analysis [13].
Advancements in smartphone technology such as batter-
ies, user interfaces, and material [14], are expected to aid
the development and adoption of AHEM systems.
Manufacturers of residential equipment and appliances
continue to embed additional sensors and control capabil-
ities in new, smart home appliances that are internet-
ready, can respond to requests from service providers,
and offer advanced cycle controls such as multi-mode or
variable speed controls and fault diagnostic sensors for
space-conditioning equipment and "eco" modes for
dishwashers, clothes washers, and other major appliances
[15].
Integration of energy services into other networked
product offerings, such as security systems and television
and telephony service.
A key strategy to engaging all stakeholders may lie in
changes to the end-user electricity pricing structures from
fixed tariffs to dynamic prices that may change several times
over a day that reflect the use of the assets on the grid at any
given time. If these structures are implemented to provide a
tangible financial incentive for customers to respond to the
requests of the service providers for demand reduction, the
customers can receive measurable monetary value for their
participation, in addition to the increased reliability of their
service. Financial incentives are but one motivating factor for
the adoption of smart homes.
C. Smart Home Energy Management
Large-scale demonstration efforts have thus far approached
smart home research with a strong utility focus and less
homeowner focus. Currently the incentive for homeowner
participation is limited to relatively small financial gain via
utility pricing structures; otherwise the motivation is primarily
altruistic (i.e., environmental benefits). Most utilities offer
incentives for energy upgrades such as attic insulation or
ENERGY STAR
®
appliances and many have leveraged load-
shedding technologies that cycle air conditioners during peak
load events. Increasingly, utilities are funding more elegant
efforts for on-request load reduction in the residential sector.
For example, CPS Energy of San Antonio, Texas has
partnered with Consert Inc. to demonstrate a load reduction
system that can alter air conditioner and water heater set
points and pool pump operation at the end-user facility during
peak load times to enable substantial peak savings with
limited impact on their customers [16]. This system is being
rolled out to most residential customers in the San Antonio
service territory.
Some utilities such as ComEd provide near-real time data to
homeowners, along with several pricing structures and load
reduction requests [17]. Many companies have recently
incorporated web-based user interfaces, so a homeowner can
adjust thermostat settings or turn off lights from a smartphone,
as shown in Fig. 3 , or a web browser [15].
Fig. 3. A smartphone app for smart homes. Photo sourced from NREL
database, PIX 20284.
Advanced grid measurements using AMI infrastructure are
being rolled out in California and Texas [18]. These projects
have multipronged focus of better integration of renewables,
enhancement of efficiency, and optimization of consumer
demands with utility needs on a community scale. Emerging
nonintrusive load measurement systems can provide enabling
data, but these modern measurement techniques are not yet
robust, accurate, easy to install, or cost-effective for integra-
tion at the meter [19]. The available legacy methods for load
disaggregation use algorithms supplemented with estimation,
so the results may have less relevance to a given household
than across an aggregated population [ ].

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References
More filters

Journal ArticleDOI
Abstract: This article reviews and evaluates the effectiveness of interventions aiming to encourage households to reduce energy consumption. Thirty-eight studies performed within the field of (applied) social and environmental psychology are reviewed, and categorized as involving either antecedent strategies (i.e. commitment, goal setting, information, modeling) or consequence strategies (i.e. feedback, rewards). Particular attention is given to the following evaluation criteria: (1) to what extent did the intervention result in behavioral changes and/or reductions in energy use, (2) were underlying behavioral determinants examined (e.g. knowledge, attitudes), (3) to what extent could effects be attributed to the interventions and, (4) were effects maintained over longer periods of time? Interestingly, most studies focus on voluntary behavior change, by changing individual knowledge and/or perceptions rather than changing contextual factors (i.e. pay-off structure) which may determine households’ behavioral decisions. Interventions have been employed with varying degrees of success. Information tends to result in higher knowledge levels, but not necessarily in behavioral changes or energy savings. Rewards have effectively encouraged energy conservation, but with rather short-lived effects. Feedback has also proven its merits, in particular when given frequently. Some important issues cloud these conclusions, such as methodological problems. Also, little attention is given to actual environmental impact of energy savings. Often, an intervention’s effectiveness is studied without examining underlying psychological determinants of energy use and energy savings. Also, it is not always clear whether effects were maintained over a longer period of time. Recommendations are given to further improve intervention planning and to enhance the effectiveness of interventions. r 2005 Elsevier Ltd. All rights reserved.

2,280 citations


"Electric Energy Management in the S..." refers background in this paper

  • ...A major limitation to the work on feedback efficacy is the relative lack of long-term data sets that can help evaluate persistence [59]....

    [...]


Book
01 Oct 1981

2,229 citations


"Electric Energy Management in the S..." refers background in this paper

  • ...The construct of psychological reactance describes this phenomenon [60]....

    [...]


Journal ArticleDOI
Abstract: Technology policies are one of the options available for the reduction of carbon emissions and the usage of energy. However, gains in the efficiency of energy consumption will result in an effective reduction in the per unit price of energy services. As a result, consumption of energy services should increase (i.e., “rebound” or “take-back”), partially offsetting the impact of the efficiency gain in fuel use. Definitions of the “rebound” effect vary in the literature and among researchers. Depending on the boundaries used for the effect, the size or magnitude of this behavioral response may vary. This review of some of the relevant literature from the US offers definitions and identifies sources including direct, secondary, and economy-wide sources. We then offer a summary of the available empirical evidence for the effect for various sources. For the energy end uses for which studies are available, we conclude that the range of estimates for the size of the rebound effect is very low to moderate.

1,709 citations


"Electric Energy Management in the S..." refers methods in this paper

  • ...Depending on the mechanism used to achieve DR, a rebound effect may appear in the form of a large load peak after the end of the DR event [41]....

    [...]


Book
01 Jan 1984

1,116 citations


"Electric Energy Management in the S..." refers background in this paper

  • ...This paper presents a discussion of the state of the art in electricity management in smart homes, the various enabling technologies that will accelerate this concept, and topics around consumer behavior with respect to energy usage....

    [...]


Journal ArticleDOI
Murray Patterson1Institutions (1)
Abstract: This paper critically reviews the range of energy efficiency indicators that can be used, particularly at the policy level. Traditional thermodynamic indicators of energy efficiency were found to be of limited use, as they give insufficient attention to required end use services. The specific limitations and appropriate uses of physical-thermodynamic, economic-thermodynamic and pure economic indicators of energy efficiency are also considered. The paper concludes with a discussion of the persistent methodological problems and issues which are encountered when attempting to operationalize all of the energy efficiency indicators. These include the role of value judgements in the construction of energy efficiency indicators, the energy quality problem, the boundary problem, the joint production problem and the question of isolating the underlying technical energy efficiency trend from the aggregate indicator.

851 citations


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