Joule Jotter: An interactive energy meter for metering

Joule Jotter: An interactive energy meter for metering,
monitoring and control
Prabhakar T.V, Nisha Bhaskar,
Tejas Pande
Department of Electronic System Engg,
Indian Institute of Science, Bangalore
Chaitanya Kulkarni
National Institute of Technology,
Surathkal, Karnataka
[email protected]
(tvprabs, nisha.b, pandet)
@dese.iisc.ernet.in
ABSTRACT
Utility companies worldwide are adopting Demand Side Management (DSM) methods to cope with unpredictable demands. Demand Response (DR) is a common strategy in
which the utility encourages users to change their demand
patterns dynamically, so as to have short-term reductions in
aggregate energy consumption. While users are concerned
about rising costs of electricity bills, utilities are concerned
about shortage of power and the real time-pricing they have
to pay energy generating companies to meet the peak demand and supply. In this paper we describe “Joule Jotter”
(JJ) a smart energy meter that bridges the gap between utility providers and domestic users equipped with such smart
meters. We show the scalability of our protocol integration.
Simple algorithms are implemented that work with utility
requests. Our evaluation results indicate that such energy
meters suffer additional time overhead of 1.5s due to Smart
Energy Profile (SEP) 2.0 protocol implementation.
Keywords
DRLC, SEP 2.0, DER, SMS, TOD, Pricing
1.
INTRODUCTION
In India, technology adoption in electricity usage has seen
success in several interesting ways. For example, monthly
bills generated by the utility company required transparency.
Human error in reading the electricity meter now uses a
camera picture to digitally record the reading. This picture
is transmitted to the central database for generating the
monthly consumption bills. Pre-paid electricity payments
have seen the adoption of scratch cards that are available
at convenient outlets such as gas stations, shops and retail outlets. While users are concerned about their monthly
bills, utility companies have issues to source power by way of
higher payments to generating companies. Utilities pay for
power based on “real-time” price but only charge “flat rate”
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from domestic users. In the event of peak demand exceeding the supply, utilities target home segments by temporarily cutting power supply; commonly called “load shedding”.
It is not uncommon to have 6 to 8 hours of load shedding
in suburban areas, followed by many hours of low voltage.
Thus utilities earn the wrath of domestic consumer who constitute the 30% of market segment of power consumption.
Rising costs of electricity bills and lack of empathy by the
utilities has prompted home users to look for new solutions.
Several brands of “smart energy meters” have appeared in
the markets with the end user in focus. Users are seeing benefits because instantaneous energy consumption feedback is
now available. Interestingly, the smart energy meter appears
to have arrived like a boon to utility companies as well. Utilities are now offering the domestic users with incentives with
a view to reduce the gap between peak demand and supply.
For example, the user may switch on the electric geyser in
the late evening instead of the regular morning peak hours.
Washing machines might have to be operated late afternoon
for “cupboard dry” clothes.
For utility companies, DSM mechanisms and DR programs are required to handle and service short time scale
increases in peak energy consumption. This requires that
users have to face the reality of real-time pricing and thus
move away from the traditional “average annual cost” model
based flat pricing. Electricity consumers have to adjust to
prices and create elasticity by altering the demand, particularly over short time frames. For example, on a particular
day, between 8PM-9PM the price from was 2.9 cents/kWh
and 1.9 cents/kWh between 12 am - 1 am [1]. The Bangalore Electricity Supply Company (BESCOM) in the state of
Karnataka, India, is perhaps one of the earlier utility companies to recognize the need for enabling DSM in its power
distribution network to manage the peak energy consumption.
BESCOM services extend to about 30% of the state’s 60
million population and serves 8 districts out of the 30 districts with 3 zones to administer. The domestic users are
offered flat pricing with 3 or more slabs. One of the tariff
categories from BESCOM is the Time of Day (TOD) pricing
for Low Tension supply users. There can be an increase or
decrease in pricing from the normal price between 06-00 Hrs
and 22-00 Hrs. Recent statistics indicate that the month of
July 2013 observed a 1000MW shortage between 06-00 and
09-00 Hrs [2]. BESCOM has also launched “Happy Hours”
for home users in the month of August 2013. The goal of
this pilot project is to observe if consumers can bring down
their consumption by 10% on average and by 15% during
peak hours.
It is now apparent that end users have to learn more about
tariffs almost on a daily basis if they have to check their
growing monthly bills. BESCOM will soon start collecting
“Fuel Cost Adjustment” charge from end users to pass on
the burden of real-time pricing. This will appear as a variable component in bills and is essentially short time scale
changes in cost of energy generating fuels. Clearly utilities
and customers have to work in tandem if peak demand and
supply have to match in the long term.
Figure 1:
loads
The big picture:
Managing deferrable
Fig 1 shows the big picture of a smart grid based home
energy supply system. Power from the utility is supplied to
a home through an energy meter that records the household
consumption. Assume for the moment that the utility company makes available the pricing profile to the smart meter
connected to the Home Area Network Gateway(HAN). The
HAN is expected to have the knowledge about the possible list of electrical appliances and the time at which they
can be turned on to support the utilities pricing profile. Although final decisions are made by humans in the loop, most
smart energy meters are unable to interface successfully with
smart grid based utilities. In this paper, we build a smart
energy meter called “Joule Jotter”(JJ) that can interface well
between domestic user and their utility companies. Apart
from building the JJ, our contributions in this paper are:
(a) SEP 2.0 implementation on the embedded Wi-Fi based
JJ smart energy meter. (b) Seamless integration between
the Internet and mobile phone operators (c) Seamless mapping between utilities and end users. TOD charging, happy
hours and other models available from utilities are supported
within pricing profiles. These are then used by end users to
effectively manage their electrical appliances.
2.
LITERATURE
The importance of smart meters for users and utilities is
brought out in work of [4] where the benefits to users include
transparency in billing and information about energy consumption. For utilities it is a way to ensure real-time pricing and change in user behavior to reduce the peak demand.
Most literature mention that energy consumption feedback
is extremely important since it brings the end user into the
loop. For instance, in [5] the authors describe an android
smartphone application that provides feedback on energy
consumption profile of several home appliances. Load disaggregation is required since a single energy meter is used.
The value proposition comes because of comparison of energy consumption with peer user homes and consumer organizations. The data obtained from the energy meters is
provided as a feedback to influence user’s behavior. The
work in [6] stresses the need for energy meters with reduced
usage barrier. User feedback is provided on a real-time basis
at a device level and thus biggest energy guzzlers are easily
identified. A load disaggregation algorithm from Markus et
al [7] propose a disaggregation algorithm called “AppliSense”
that breaks down the energy consumption to a device level.
Prior to the SEP 2.0, there were several works that proposed
middleware frameworks. The authors in [8] propose a middleware framework called “Hydra” that facilitates intelligent
communication through a P2P network. Smart energy plugs
called “Ploggs” were used to gather energy usage statistics
from electrical loads. However, the proposed architecture
does not integrate utility providers and end users. GSM
Automatic Power Meter Reading System (GAPMR), a prepaid payment based system is proposed in [9]. The number of units consumed is communicated to utility companies
with help of GSM to monitor power theft. Manisa et al [10]
have performed a thorough analysis of the load profiles of
typical house-hold appliances and have suggested that while
electric clothes dryers and water heaters provide the highest DR opportunity and clothes washers, refrigerators and
electric range ovens offer the least potential. Field trial in
50 households by Smartcity M´
alaga [11] studies the effect of
energy monitoring. It is found that 42% of the participants
have achieved over 10% consumption reduction, 33% have
seen minimal change (within 10%) and the remaining 25%
have increased their consumption more than 10%. As stated
earlier, this corresponds to the figures BESCOM is attempting to achieve. PEAKSAVE [12] has implemented a similar
system using their smart meter named S Plug. It provides
both monitoring and control functionalities. The work in
nPlug [13], shows that it is possible to reduce peak energy
demand for utilities by formulating Demand Side Management (DSM) strategies and schedule high power consuming
appliances. nPlug, a inexpensive smart plug, runs a decentralized DSM strategy by assessing supply-demand gap.
Users are expected to physically key-in their preferences indicating the starting and end timings to schedule their appliances. The JJ on the other hand provides users feedback
about their consumption aligned to the pricing profile of the
utility provider. Thus there is a seamless interaction between utility providers, home users, Internet cloud and the
mobile networks.
3.
PROTOCOL INTEGRATION: SEP 2.0
WITH INTERNET AND MOBILE
NETWORKS
Our hardware comprising of the JJ and HAN together
ensure that all protocols are integrated and run in a seamless manner. The SEP 2.0 standard [3] supports several
resources such as subscription and notification, end device
and response. The SEP 2.0 supports the RESTful architecture. Such an architecture does not differentiate between a
client and server in terms of resource representation, both
in terms of resource exposure and interaction. SEP 2.0 is
built around the concept of resources and function sets. Re-
sources can be described to be the properties of a physical
meter. A set of resources when used together to implement
a specific function make a function set. Function sets such
as metering, demand response and load control (DRLC) and
pricing provide the seamless communication between utilities and home user smart meters. The load control is enforced using the DRLC protocol using the publish-subscribe
resource available under SEP 2.0. Here, the load side JJ is
the DRLC client that runs the subscription server and the
HAN is the DRLC server that publishes resources and sends
notifications. The JJs are also equipped with actuators to
enable turning off the loads.
Figure 3: Joule Jotter
Figure 2: Overview of System Implementation
The SEP 2.0, although available on hardware (for instance
CC2538) platforms, we chose to implement the standard on
Wi-Fi. The idea is to support scalable architectures ranging from single homes to multiuser dwellings. Fig 2 shows
the overall protocol integration in our implemented system.
The outermost ring has the communication protocols to end
user and utility provider. The second ring exposes the user
to the SEP 2.0 implementation. For example, the control
of loads and metering information is available to the user
as a simplified service. These work closely with the prepaid and post paid payment models to which the user might
have subscribed. The SEP 2.0 implementation on JJ and
HAN is shown in the third ring. Finally, the physical hardware is represented in the innermost circle. The HAN implements the pricing profile and directly interacts with the
utility companies. It also has the SMS gateway capability.
Thus the complete system facilitates integration of various
services and payment models e.g. flat rate, TOD, happy
hours, prepaid, postpaid etc. The end user can easily use
these services via SMS, Email. In the event the user is at
home, an android application running on the phone connects
to the home network to manage the electrical appliances.
4.
JOULE JOTTER: SMART ENERGY
METER
Fig. 3 shows a snapshot of JJ, the smart energy meter.
The controller chosen is MSP430F5438A from Texas Instruments. It supports a single phase electronic energy meter
sensor chip ADE7953 from Analog Devices that meters almost all electrical parameters such as current, voltage, power
factor, etc. The current input is obtained from a current
transformer (CT) and the voltage input is obtained from a
step down potential transformer (PT). The JJ has a pro-
vision for local storage of all metered data. Additionally,
the board has support for TCP/IP communication protocol
stack over a IEEE 802.11b/g Wi-Fi interface. The system
clock frequency is 25 MHz with support for 16 bit timers.
The system has support for SPI, UART and I2C modes. The
ADE samples at a rate of 895 kHz. The Wi-Fi chip CC3000
operates over the 2.4GHz frequency range with +18dBm of
maximum transmission power. We measured the current
consumption of the JJ first during a write operation and
then during a Wi-Fi transmission. We found that it requires
average of 60mA and 170mA respectively.
Figure 4: Wash cycle of a washing machine
Fig. 4 shows the load profile for a washing machine. The
system is rated for 545 watts. Power consumption data for
four wash cycles were recorded by the JJ on its SD card.
When water is pumped into the machine for soaking the
clothes and rinsing at the end of washing, a power of 4 watts
is required. A wash cycle and a spin dry cycle require significantly higher amounts of power. Often, the power drawn
exceeds the rated power from the manufacturer; although
only for a short interval of time. In the event of a power
failure after the start of the program, the washing machine
is able to “auto-restart” from wherever it stopped. This is
just an example of all the loads for which we are able to
record the power consumption in time.
4.1
Support for SEP 2.0
SEP 2.0 is the application level protocol that we have implemented to support all the algorithms. Our implementa-
tion works on top of Wi-Fi and TCP/IP supported CC3000.
The data is encapsulated as “XML” packets and transferred
over HTTPS. The application protocol was developed to run
on the MSP430 microcontroller. The CC3000 is accessed by
the controller through API’s(application programming interfaces). The host issues API function calls to CC3000 and
the module responds by performing the appropriate activity.
Events are triggered by the CC3000 device as it issues interrupts to the host. Command response events are in response
to commands issued by the host and unsolicited events are
triggered asynchronously by the CC3000 device to indicate
occurrence of a system event.
4.1.1
Pricing Profile
SEP provides a very flexible and scalable structure for
pricing profile implementation which the utility service
provider issues. Each pricing profile itself is given by TariffProfile. Each TariffProfile will have a list of RateComponents. Each RateComponent specifies the type of user depending on the consumption rate. Since the pricing profile
is a generic package which can be customized depending on
the resource being metered, for electricity metering applications, RateComponent is measured in terms of kWh. Each
rate component has a list of TimeTarifIntervals. For example, when a time-of-use tariff with rates varying for each
hour is being used, the list will have a total of 24 TimeTariffIntervals (tti). Now, each of these tti can further have
a list of ConsumptionTariffIntervals (cti). These again are
split up depending on the energy consumed. The idea is that
the price changes (increases) when the energy consumed increases. The cti can be used in case the tariff profile requires
it, otherwise, we set the list to hold one object which has a
price associated with it.
4.1.2
Demand Response Load Control(DRLC) Function Set
sets a threshold for the power consumed by the load. On
start of the event, the DRLC client will indicate the same to
the server. If the threshold is exceeded, then the Subscription server notifies the client. The DRLC server exposes
its EndDeviceControl object to the DRLC client indicating
for the client load to be turned off. This implementation
can be easily extended to multiple smart appliance scenario.
The server can set thresholds for each of these appliances or
for the entire system. The server is notified every time the
threshold is exceeded to ensure that the condition is satisfied
at all times. While this implementation is a general protocol, its elegant use mostly relies on control algorithms and
mechanisms that exploit it. For example, client can switch
on once the EndDeviceControl replies with a “yes” under
conditions such as:(a) automatic increase in threshold, (b)
Price tariff changes in time and (c) a time-out occurs.
4.1.3
5.
In our implementation of DRLC, each smart appliance
will serve as the DRLC client. The DRLC server is implemented on the HAN. The DRLC function set is supported
by the SubscriptionNotification resource. Fig. 5 is a snap
shot of our implementation. It shows that soon after the
discovery procedure, the subscription client will first subscribe to the server’s metered resource. The subscription
SCENARIOS FOR DEPLOYMENT
Our protocol integrated architecture is exploited in several ways that demonstrate its scalability. In this section,
we present three scenarios for our home energy monitoring system. Our implementation stresses on a highly flexible architecture which can be customized depending on user
preferences and requirements. We look mostly at integrating the Internet and mobile network based protocols with
the SEP 2.0 based existing infrastructure. The user is of
paramount importance in our architecture. The complete
user application was built over a smart phone as an android
app. The basic requirement is that user should be able to
respond to the requirements of the house with minimal inconvenience caused either due to physical location or any
other restrictions. In the event the user is at home, direct
HTTP communication is enabled. All deployment scenarios
are capable of running simple algorithms.
5.1
Figure 5: DRLC with multiple clients
Metering Function Set
SEP’s metering function set requires implementation of
the metering client on the HAN which regularly pulls the
metered values from the metering server implemented on
the JJ. The HAN also implements the pricing function set
which receives the price tariff from the utility provider. As
an example, the profile can be a TOD profile with consumption slabs; the prices being higher for a higher consumption
slab. Since the HAN already knows the power utilization of
the appliances from the metering function, the appropriate
price for that overall consumption can be extracted. Subsequently, the DRLC function set can be used to switch off
any of the appliances when the power utilization exceeds a
certain set threshold on the HAN. This threshold can either
be specified by the user or the utility provider.
Scenario 1: Joule Jotter with DNS support
In the first scenario, we assumed our user to have deployed one or two (a very small number) smart meters at
home. The pricing profile along with DRLC runs directly
on one of the JJs and is represented as home server in Fig. 6.
The steps shown in Fig. 6 were implemented to control the
appliances. To ensure that there is complete protocol integration, we subscribed to an online tool called SMS Global
service; also shown in Fig.6. This service accepts HTTP
connections from the home server JJ and delivers an SMS
to end user. The SMS from the end user is forwarded to a
play in our implementations and is part of the loop. The
android app is built in such a way that the user can check
the power consumption details in either energy units(kWh)
or in terms of money. The algorithms interact with the user
by providing options for the load(s) to be controlled.
6.1
Flat Rate Threshold Algorithm
Uniform Resource Locator (URL). This URL is registered to
the server JJ with a free dynamic domain name system (dynDNS) service. This scenario supports direct communication
with the JJ and load control is possible either one-to-one or
one-to-few.
Assume that the utility offers a fixed rate pricing profile
for the first 100 units consumed by the user. The HAN has
the power profile of each appliance. In this implementation we set a threshold for the average power (including all
appliances). Algorithm 1 will compute the total energy consumed in real time and ensure the average does not exceed
the set threshold (3 units/ day). In the event of exceeding
the threshold, the system presents the user with a choice
of presently active appliances (single or a set of them) that
can be turned off to optimally lower the consumption below
the threshold. The appliance(s) that brings the power consumption below the threshold and closest is recommended
to the user. The user selected option is used to control the
appliance.
5.2
6.2
Figure 6: A protocol integrated architecture
Scenario 2: HAN - JJs in star topology
In this scenario, we have a HAN that runs the pricing
profile and the SMS gateway service shown in Fig. 6 as
the home server. It is however still possible to use external
SMS service with the HAN registered with the DynDNS.
This scenario is envisaged for multi dwelling units and large
homes where several smart meters are required for plugged
loads.
6.
EVALUATION AND RESULTS
Algorithm 1 Flat Rate Threshold
T Ep ← T otal energy consumed till now
T h ← T hreshold energy
JJArray ← Array of active appliances
JJT empArray ← T emporary array
Ep ← Energy of an appliance in JJArray
Ensure: T Ep ≤ T h
if T Ep > T h then
for i = 0; i < sizeof (JJarray) − 1; i + + do
if (T Ep − JJArray[i]− > Ep) < T h then
JJT empArray[k] = JJArray[i]
k++
end if
end for
if JJT empArray 6= Φ then
sort(JJTempArray wrt energy value)
end if
Energies of two or more devices are added and compared with TEp
The group of devices which crosses threshold are sorted
inJJTempArray
send the JJTempArray to user and control the appliance(s) based on his feedback
end if
To demonstrate that our integrated approach has the necessary freedom to adjust the JJ to upcoming business models, we implemented three control algorithms. We now describe them in detail. The user has an interactive role to
Happy Hours
The “Happy Hours” algorithm is also utility provider driven
and provides an option to users to reschedule their appliances. The algorithm computes the tariff a user incurs in
happy hours compared to time outside these hours. Algorithm 2 shows our implementation.
Algorithm 2 Happy Hour Pricing
C[i] ← Rate f or ith consumption interval
n ← Span of device operation
h ← Happy hour consumption interval
E[k] ← Energy consumption in kth interval k ∈ (1, n)
JJArray ← Array of active appliances
JJT empArray ← T emporary array
Cwoh ← M oney spent at current time
Ch ← M oney spent in happy hours
Cmin ← M in. money f or user to consider rescheduling
JJT empArray = JJArray
sort (JJTempArray wrt Energy)
n,i+n
P
Cwoh =
E[k] ∗ C[t]
Ch =
k=1,t=i
n,h+n
P
E[k] ∗ C[t]
k=1,t=h
if Ch − Cwoh > Cmin then
send user notification to schedule the appliances
end if
if user rejects then
Repeat the algorithm for next element in JJTempArray
end if
6.3
Pre-Paid Accounts
In this algorithm user can top-up his account for any
amount and the home monitoring system keeps track of his
balance. Algorithm 3 shows that the pricing profile supported by SEP 2.0 is amenable for this support. Once the
time tariff interval is recognized based on time of the day, the
energy consumed is compared with the consumption slabs
for that time interval and the appropriate price is extracted.
Table 1 shows the results of our SEP 2.0 stack evaluation.
The table shows the average delay with and without the SEP
2.0 stack. As can be seen, E-mail takes a 1 sec more with
the protocol integrated stack. With SMS running on the
HAN, the system takes a little over 1 s with the integration.
The SMS Global solution resulted in an average delay of 48
minutes from the user to the home.
Algorithm 3 Pre-Paid Tariff
C[i] ← P rice f or ith consumption interval
S[i] ← Start value f or ith consumption interval
T Ep ← T otal energy consumed till now
T c ← Consumpstion cost of appliance
T h ← T hreshold balance
BU pdate ← U pdated balance
Ensure: BU pdate > T h
for i=(noOfConsumptionTariffInervals-1);i>=0;i– do
if T Ep > S[i] then
S[i + 1] = T Ep
for (j = i; j ≥ 0; j − −) do
T emp = T emp + C[j] ∗ (S[j + 1] − S[j])
T c = T emp
end for
end if
end for
BU pdate = BU pdate − T c
if BU pdate < T h then
Send warning to user phone
end if
Table 1: Latency
Email
(custom)
JJ to user 5.98
user to JJ –
7.
Measurements (secs)
Email
SMS
SMS
(SEP)
(custom) (SEP)
6.9
24.63
26
–
13.76
15
DISCUSSION
The protocol integration has resulted in seamless and scalable solutions for home and multi dwelling units. Unlike the
hardware version of ZigBee SEP 2.0, our software implementation of the stack yielded elegant and simple scenarios. For
example, our experiments with the SMS Global service, although requires to improve the time guarantees, it provided
insights that a smart energy meter is now manageable from
anywhere in the world. The algorithms have shown that
adding variants is easy. For example, the average power
threshold could be replaced with peak power. The TOD
can be replaced with happy hours. The pre-paid algorithm
can be tuned to provide daily alerts for the post-paid scenario. The algorithm complexity of the flat rate algorithm
is N + N logN , and that for happy hours is N logN followed
by pre-paid tariff algorithm being n2 . Here, N is the number of JJ’s and n is the number of consumption tariff intervals(cti’s).
8.
CONCLUSIONS
In this paper, we built a smart energy meter called Joule
Jotter from scratch and implemented Smart Energy Profile 2.0 in software over Wi-Fi. The integration with mobile
networks and Internet has yielded many deployment options
and seamless interaction between utilities and the end user.
Existing algorithms running on smart meters use the philosophy “pay more use more”. The JJ makes a paradigm shift
by aligning the power consumption to utility requests. We
believe, quite like water consumption, energy consumption
should be based on global optimization algorithms.
9.
ACKNOWLEDGMENTS
We thank ACM SIGCOMM - Community Grant for generously funding this IoT device, RBBCCPS, IISc for the seed
funding and our partner TUDelft who have stood by us.
10.
REFERENCES
[1] “Ameran Illinois official website” Available:
http://www.powersmartpricing.org/
[2] “BESCOM official website” Available:
http://bescom.org/en/dsm-activities-in-bescom-2/
[3] ZigBee Alliance, Inc. and HomePlug Powerline
Alliance, Inc. Smart Energy Profile 2: Application
Protocol Standard, 13-0200-00, April 2013.
[4] Jixuan Zheng, Li Lin, et al. Smart Meters in Smart
Grid: An Overview. In 2013 IEEE Green Technologies
Conference, pages 57-64 , Denver, CO , 4-5 April 2013.
[5] Markus Weiss, Friedemann Mattern, et al. PowerPedia:
changing energy usage with the help of a
community-based smartphone application. Personal
and Ubiquitous Computing, August 2012.
[6] Markus Weiss, Friedemann Mattern, et al. Handy
feedback: Connecting smart meters with mobile
phones. Proceedings of the 8th International Conference
on Mobile and Ubiquitous Multimedia, Cambridge, UK,
November 22-25, 2009, ACM.
[7] Markus Weiss, Adrian Helfenstein, et al. Leveraging
smart meter data to recognize home appliances. IEEE
International Conference on Pervasive Computing and
Comm., pages 190-197, Lugano, 19-23 March 2012.
[8] Marco Jahn, Marc Jentsch, et al. The Energy Aware
Smart Home 5th International IEEE Conference on
Future Information Technology, Busan, May 2010).
[9] Rohit Nargotra, Ritula Thakur, et al. Design of a
Prepaid Power Meter with Communication facility
based on GSM Network International Journal of
Scientific and Engineering Research, Jan 2013).
[10] Manisa Pipattanasomporn, MuratKuzlu, et al. Load
Profiles of Selected Major Household Appliances and
Their Demand Response Opportunities. IEEE
Transactions on Smart Grid, 07 August 2013.
[11] Jaime Caffarel, Igor G´
omez, et al. Lessons Learned on
Home Energy Monitoring and Management: Smartcity
M´
alaga. Proceedings of the fourth international
conference on Future energy systems, pages 263-264,
ACM NY, USA, January 2013
[12] Jeonghoon Kang, Jaechul Kim, et al. PEAKSAVE:
Energy Monitoring Service. Proceedings of the 10th
ACM Conference on Embedded Network Sensor
Systems, pages 367-368, NY, USA, November 2012
[13] Tanuja Ganu, Deva P. Seetharam, et al. nPlug: A
Smart Plug for Alleviating Peak Loads. e-Energy ’12
Proceedings of the 3rd International Conference on
Future Energy Systems, ACM NY, USA, May 2012

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