Hot Water DJ: Saving Energy by Pre-mixing Hot Water

Hot Water DJ: Saving Energy by Pre-mixing Hot Water
Md Anindya Prodhan
Kamin Whitehouse
Department of Computer Science
University of Virginia
Department of Computer Science
University of Virginia
[email protected]
[email protected]
Abstract
After space heating and cooling, water heating is typically
the largest energy consumer in the U.S. homes, accounting
for approximately 17% of total energy consumption. Current
water heating systems waste up to 20% of their energy due
to poor insulation in pipes or water tanks, but improving this
insulation is too costly to be practical for energy savings. In
this paper, we build on recent fixture and water flow monitoring systems to create the Hot Water DJ, which provides
hot water to any fixture based on the requirement of the fixture. In our experiment, we deployed sensors in a real home
to learn about the accurate temperature model for each of the
fixtures in the house. Whenever any of the fixture asks for
hot water, Hot Water DJ would provide hot water as hot as
the appliance typically require. Our result shows, with our
approach we can save 10% of water heater energy with limited impact on user comfort and cost.
Categories and Subject Descriptors
C.3 [Special-Purpose and Application-Based Systems]: Real-time and Embeded Systems
; H.1.2 [Models and Principles]: User/Machine Systems—Human Information Processing
General Terms
Design, Experimentation, Economics, Human Factors
Keywords
Water heater, Energy consumption, Mixer, Wireless sensors, Residential homes
1
Introduction
Water heaters are the second biggest energy consumer in
homes after HVAC and account for approximately 17% of
their energy usage [1]. Even with only 25% penetration, 10%
of water heater energy accounts for around 0.1% of the national energy budget which is equivalent to making jet fuel
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Copyright 10% more efficient. Therefore, making water heaters efficient is an important problem to address.
Most of the homes in the U.S. typically use a single water heater for the whole house which is either a tank based
water heater or a tankless water heater. Tank based water heaters keep hot water in insulated storage tanks which
causes significant energy loss due to stand by loss . Tankless
water heaters reduce stand by loss by heating the water on
demand. However, both types of central water heaters must
run hot water to the fixtures through pipes, where the hot
water remains after the fixture is turned off and the heat is
lost. This energy waste is called pipe loss and studies show
it to account for more than 20% of water heating energy in
the typical home [12]. Today, the main solution to this problem is the so-called point-of-use (POU) water heater. POU
water heaters are installed just below the fixture they serve,
and therefore do not encounter any pipe loss. However, the
main downside of a POU heater is that for a large house with
many fixtures, several POU water heaters would be required,
which increases cost and makes the installation difficult and
cumbersome. All the approaches described above considered mechanical changes to improve the efficiency of the
water heaters. However, in this project we view energy efficiency of water heaters from a computational point of view.
In this project, we propose a Hot Water DJ, which will
strategically provide water to each fixture based on some
simple sensing and control. From our background analysis
on typical usage of hot water in homes, we observed that every fixture requires different water temperatures on average,
and that each fixture is subject to different pipe lag depending on the structure of the pipes. We also observed that, in
many cases, people turn on the hot water tap, but turn it off
again before the hot water even reaches the fixture. Hot water for such events is entirely wasted. Upon further investigation, we found this to be most common at the bathroom
sink when people may have soap or a toothbrush in the right
hand, and use the hot water only because they turn the fixture
with their left hand.
Based on these observations, we propose a Hot Water DJ
that intelligently provides water at a different temperature
for each fixture, in order to minimize energy waste due to
hot water remaining in the pipes. It does this by pre-mixing
the hot water with cold water before running it through the
hot water pipes, instead of at the fixture. Thus, when the
fixture is turned off, the water that remains in the pipe is
Figure 1. Hot Water DJ uses flow sensor at the water mains (a), and temperature sensors and water flow sensors in the
fixtures (b).
at a lower temperature, thereby reducing energy waste. In
some cases, it can also opt to delay the hot water altogether
if the hot water is likely to never reach the fixture before
the user turns it off. In order to function, the Hot Water DJ
must know which fixtures are currently being used, and at
which temperatures those fixtures typically operate. To do
this, it leverages and builds upon recent sensing innovations
such as HydroSense, WaterSense, and NAWMS [9], [15],
[10] that allow simple and inexpensive fixture and water flow
monitoring.
2
Through modeling and analysis of 6 days of empirical
data traces from our test home, we estimate the energy used
by a conventional central water heater and the amount that
would be saved by the Hot Water DJ. Our analysis indicates
that, our approach can save more than 10% of the water heating energy with limited effect on user comfort.
2.1
The Hot Water DJ could be added to an existing home as a
retrofit, and would work with either a tank-based or tankless
heater. Because it requires only a simple set of sensors and
two valves, it could also be incorporated directly into water
heating systems by the manufacturer, perhaps for the marketing benefit of being “10% more energy efficient”. Hot water
heaters in the US have a typical lifetime of about 20 years, so
adoption by manufacturers would produce widespread adoption within 20 years.
The rest of the paper has been organized as follows. In
Section 2, we discuss the state of the art water heating solutions and some related works on water heaters. We introduce
the results of our exploratory study in section 3. We describe
our proposed system in Section 4. The experimental setup of
our study is presented in Section 5 and corresponding performance evaluation of our Hot Water DJ is presented in Section 6. Section 7 points out some limitations of our study.
We conclude with a brief summary in Section 8.
Background and Related Study
The water heater is one of the most common appliances
in homes, especially in cooler countries. As water heaters
are the second largest energy consumer in homes, a lot of research has been going on to make water heaters to be more
efficient. So far, most of these evolutions have been based on
mechanical changes. Some computational analysis on water
heaters have been pursued, but those mostly have considered
water monitoring without focusing on the energy consumption.
General background on water heaters
Today there are several commercially available water
heating solutions present in the market and every generation
these water heaters are becoming more refined and more energy efficient. Today electrical water heaters can even have
an efficiency of 98%, but these heaters generally does not
consider the losses they suffer due to the piping structure of
the house.
Commercial water heaters can be generally of two types
based on their usage in the house. Tank based water heaters
and Tankless water heaters are more popular in the U.S. as
they provide a whole house solution. On the contrary, pointof-use water heaters are generally used for one or two fixtures and are more popular in Europe and southern Asia.
Most U.S. households use tank water heaters [3] as they
are the least expensive. However, tank based water heaters
also consume the most energy as they suffer from stand by
heat loss. Stand by heat loss is defined as the energy required to maintain the water at a preset temperature in a
large tank due to imperfect insulation. On the other hand,
tankless water heaters [2] heat water only when somebody
asks for it. They are often more energy efficient than the
tank based water heaters as they do not suffer from standby
energy. However, the tankless solution is much more expensive compared to the tank based solution and installation is
Figure 2. Models for the fixtures: (a) Temperature distribution of hot water used in individual fixtures (b) Distribution
of pipe filling time for each fixtures when hot water was eventually used
more difficult and hence costlier. Both tank and tankless water heaters share a common problem called pipe loss. Whenever hot water is used, some of the water is trapped inside
the pipe after the fixture is off and eventually the water gets
cold. Thus, this heat is wasted into the pipes. This kind
of energy loss is called pipe loss. The amount of pipe loss
mainly depends on the piping system of the house and the
position of the water heater [11]. Lutz [12] provides an estimate of the pipe loss based on the Residential End User
Water Study (REUWS) database [14] by showing that 20%
of water energy is wasted in the pipes for showers, sinks and
dishwashers in a single family residential home.
Point-of-use water [7] heaters are a miniature version of
tankless water heaters that are generally installed just under
a specific fixture to eliminate pipe loss. However, for a typical house we would need several point-of-use water heaters
to serve all the fixtures and appliances of the house, which
translates to higher cost of installation due to the difficulty of
retrofitting existing plumbing systems.
2.2
Water monitoring solutions
Several approaches has been proposed in the literature
to infer fine-grained water fixture usage in homes. In
NAWMS[10] vibration sensors are used on pipes to disaggregate total flow measured at a central location in an unsupervised manner. Froehlich et al. [9] uses a single pressure
sensor plugged into a free spigot or water outlet in the home
to disaggregate water flow into individual fixture events
based on water pressure signatures. In WaterSense[15] Srinivasan et al. used passive motion sensors along with utility
water flow meters to disaggregate water usage in a typical
home. Our presented proposal will build on these solutions
to identify fixtures and water flow for any water event, as
none of these systems actually deal with hot water losses or
energy waste due to these losses.
3
Exploratory Study
We conducted an in-situ study in a real home with two
people for six days (from 24th Jun 2012 to 29th Jun 2012).
Our test home was instrumented with several sensors like:
water flow meters, z-wave contact sensors, e-monitor sensor and temperature sensors. Water flow meters were instrumented on both hot and cold water pipe to identify the water
flows for both hot and cold water. In our home deployment,
we used a Shenitech Ultrasonic water flow meter [5] that
uses the Doppler effect to measure the velocity and resulting flow of water through the pipe. The flow meter reports
instantaneous water flow (in cubic meters per hour) at a frequency of 2Hz using the homes Wifi connection to transmit
data. We used z-wave sensors and emonitor sensors to identify water fixtures for any water event. In our test home, we
used aeon lab’s z-wave sensors [8] which detect every fixture on/off event and notify them wirelessly to a z-wave controller attached with the home base station. Emonitor sensor
gives the instantenious power consumption of any electrical
circuit. In our test home we install e-monitor sensors to the
dishwasher and washing machine. We used the power signatures of these devices to identify when they are turned on/off.
We also used temperature sensors to measure the temperature of the water coming out of the fixture. These temperature sensors are used as a ground truth for water temperature and pipe lag calculation. We instrumented each fixture
with a LM35DZ [4] temperature sensor from National Semiconductor Corporation. These sensors are used to measure
the instantaneous water temperature in 0 C at every second
and report it back to the home base station wirelessly using
RF200 snap engines [6]. Finally, we added a water presence
sensor to each fixture by placing two wires within 2mm of
each other in the direct path of water flow. When the water
fixture is turned on, the SNAP engine detects the presence of
water via connectivity between the two wires. We placed the
Figure 3. A typical shower event: (a) Mixing of hot and cold water which can be used to determine the water temperature
and pipe lags for the event (b) Ground truth pipe lag time and water temperature measured through a temperature
sensor attached to the shower head
temperate and water presence sensors on the kitchen sink,
the bathroom sink, and the shower.
The goal of this study is to understand the nature of the
waste caused by current water heating solutions, and figure
out which ones can be addressed through intelligent sensing
and control. Through analysis of the available data, we arrived at three new observations:
3.1
Observation I
The first observation is: every fixture requires different
temperature water. This is because people use different temperature to wash their hands, to wash dishes and to take
showers, and usually each fixture has its own purpose. In
our test home, there were three different fixtures. Figure 2(a)
shows the temperature distribution for the fixtures.
3.2
Observation II
Our second observation is: every fixture has different pipe
lag, and hot water often never reaches the fixture. In a typical home each fixture is in a different distance from the water heater due to the piping structure of the home. As a result, the time required for the hot water to reach the fixture
head (which we define as pipe lag) is different. Even for the
same fixture the pipe lag is highly variable as often some fixtures are placed in serial along the same piping infrastructure
which causes interaction between fixtures and their pipe lag
varies when their usage events are close in time. Figure 2(b)
shows the distribution of pipe lags for the individual fixtures.
In the study, we also figured out there are several hot water
events for which the duration is so short that the hot water
could not reach to the fixture head within the duration of the
event. As a result, for these events all the hot water drawn
from the water heater is actually wasted into the pipe.
3.3
Observation III
Our last observation is: both the water temperature and
pipe lag can be observed with hot and cold water flow meters. Figure 3(a) and 3(b) shows a typical shower event and
the water temperature variation over the event. In Figure 3(a)
we show the water flow trace of a shower event and in Figure 3(b) we present the ground truth temperature and pipe
lag for that event. From the figures, the total water used by
a shower event can be easily split into two distinct types of
flow. The first type of flow during a shower event is at the beginning when the consumer turns the shower (or bath spigot
in a combined shower/bath) to full hot. This is how consumers get hot water to the shower head quickly. All this
flow is wasted hot water as this was the water logged into the
pipes during the last water event. We used this duration to
measure the pipe lag. When both the person and the flowing water are ready, the hot water flow is reduced and some
cold water is mixed in to achieve the desired showering temperature. This second portion of the event is the useful part
of the shower. Similar type of behavior is visible for fixture
events too. Using the mixing ratio of hot and cold water we
can very easily determine the water temperature through the
event. In order to calculate the ground truth pipe lag we define a threshold called hot water arrival threshold. For our
analysis we choose the hot water arrival threshold to be 900 F.
This is because, whenever hot water reaches the fixture head
the water temperature remains higher than 900 F and whenever hot water is not used the temperature remains below
900 F. The figure shows that the water temperature is actually
a function of mixing ratio. Pipe lag can also be calculated
from the hot and cold water flows. This is important because
this allows the temperature and pipe lag to be measured at a
single point near the water heater, instead of requiring usage
of temperature sensors on every fixture.
4
Solution Overview
In this section, we propose a Hot Water DJ, which is an
add-on to the current water heating solution, and which selects the hot water temperature intelligently for any fixture
event to save energy.
(tmpi ) and pipe lag time (lagi ) for that event. These events
are also assigned to a particular fixture based on the fixture
turn on/off information. From this data, the system must define a set of temperature and a set of delays for each fixture
so that maximum energy is saved without exceeding the allowable comfort loss.
In order to formulate the problem let us define the following terms:
Hot Water Event Set Hot water event set (HE) is the set of
all the events where the median temperature is higher
than the hot water arrival threshold.
Temperature Distribution Temperature distribution for a
particular fixture is the set of all the temperatures at
which water was used by the fixture.
Figure 4. Proposed Hot Water DJ system
4.1
User interface
For our system to work, the user simply needs to install
the mixing unit near the hot water heater, and then uses the
home fixtures as usual. Over time, the system learns the ideal
temperatures and delays for each fixture. After a while, the
system prompts the user (perhaps via email or through a web
page) to modify the per-fixture settings. The user is given a
“comfort knob” that allows the user to choose different miss
time settings. For each miss time, the system tells the user
the temperature and delay setting for each fixture. The user
keeps turning the knob until he/she finds a desirable balance
between comfort and energy savings. Sometimes user might
need water hotter than the water provided. For those cases,
we would allow the user to override the smart controller by
turning the hot water fixture on and off multiple times. For
each turning of the hot water fixture our system would increase the hot water temperature by 50 F.
4.2
Hardware design
Figure 4 shows our Hot Water DJ attached to a typical
tank water heater. In our proposed system, we use two water
flow sensors and a pressure sensor. The water flow meters
are instrumented on both the hot and cold water pipe and
they provide the instantaneous water flow through the pipes.
The pressure sensor is instrumented to the cold water pipe.
Whenever a water event occurs, the pressure sensor tells us
which fixture is being turned on/off based on the water pressure drop [9]. To provide water at the required temperature
to any fixture, our system uses a mixer on the hot water pipe.
The mixer mixes the cold water with the hot water coming
out of the tank to ensure that water reaches the fixture head
at the required temperature.
4.3
Pipe lag Distribution Pipe lag distribution for a fixture is
defined as the set of all the lag times for the events associated with that particular fixture.
Miss Time Miss time is defined as the amount of time where
the user needs hot water, but not getting it.
For our Hot Water DJ, miss time is calculated as a measure of comfort. For our system, total miss time consists
of three components: temperature penalty (TempPen), pipe
lag penalty (PipeLagPen) and additional delay (AddDelay).
TempPen is defined as the penalty for providing water to the
fixture at a lower temperature than required. If the median
temperature of the event is greater than the temperature selected for the fixture then the whole duration of the event
is accounted as TempPen. An event is penalized for its lag
time only if the selected temperature for the fixture is less
than the median temperature of the event which ensures that
those events which are already penalized through TempPen
are not penalized again. AddDelayPen is the delay Hot Water
DJ introduces to deal with short events. Thus, for any event
p of a fixture F, TempPen, PipeLagPen and AddDelayPen
are computed using the Equations 1, 2 and 3 respectively.
Here, the selected temperature and delay for the fixture is F
is tF and dlF respectively. Finally, miss time is the sum of
these penalties over all the events in HE (Equation 4).
TempPen p =
PipeLagPen p =
lag p , if tmp p < tF
0,
otherwise
(
AddDelayPen p =
Problem Formulation
The goal of the Hot Water DJ is to provide water for each
fixture event at an optimal temperature and after an optimal
delay so that the total comfort loss is less than a preset comfort loss for a given water event set. Over the course of n
days, Hot Water DJ observes the hot water flow, cold water
flow and duration for each water events in the test home. The
event set (E) is a vector of events over those n days where
each element i of the vector contains a hot water flow ( f hi ),
cold water flow ( f ci ), duration (duri ), median temperature
dur p , if tmp p > tF
0,
otherwise
MissTimeF =
∑
dlF
2 ,
0,
if p ∈ HE
Otherwise
(1)
(2)
(3)
(TempPen p + PipeLagPen p + AddDelayPen p )
p of F
(4)
We used an energy consumption model to calculate the
energy consumption for our Hot Water DJ from the water usage data trace over the 6 days and compared that
with the energy consumption of a typical tank based water heater installed in our test home. Water Heater Analysis
Algorithm 1 Maximize EnergySaved
Input: Miss Time mt, Temperature Distribution tdK , tdB ,
tdS , Pipe Lag Distribution pdK , pdB , pdS , Event Set
E
1: esmax = 0
2: for n = 0 to 100 do
3:
for m = 0 to 100 do
4:
tK = percentile( tdK , n)
5:
tB = percentile( tdB , n)
6:
tS = percentile( tdS , n)
{tK , tB and tS are the temperatures selected for the
kitchen sink, bathroom sink and shower respectively for the current iteration}
7:
dlK = percentile( pdK , m)
8:
dlB = percentile( pdB , m)
{dlK and dlB are the delays selected for the kitchen
sink and bathroom sink respectively for the current
iteration}
9:
if calcMissTime(tK , tB , tS , dlK , dlB , E) ≤ mt then
10:
es = calcEnergySaved(tK , tB , tS , dlK , dlB , E)
11:
if es ≤ esmax then
12:
esmax = es;
13:
nmax = n
14:
mmax = m
15:
end if
16:
end if
17:
end for
18: end for
19: return [nmax , mmax ]
Model (WHAM) [13] provides simplified energy consumption equation for water heaters. Equation 5, Equation 6 and
Equation 7 are used to estimate the energy consumption of
both the tank water heater and the Hot Water DJ.
Qheat =
vol × den ×C p × (Thot − Tin )
EF
Qstdby = UA × (Ttank − Tamb ) × (24 −
UA =
1
RE
Qout
)
RE × Pon
1
− EF
24
1
(Ttank − Tamb ) × 41094
− RE×P
on
(5)
(6)
(7)
Where, Qheat = Heating energy
Qstdby = Standby energy
vol = volume of water drawn per day
den = density of water
C p = Specific heat of water
Ttank = Set-point temperature of the tank
Thot = Temperature of the provided hot water
Tin = Heater inlet temperature
EF = Energy factor
UA = Stand-by heat co-efficient
Qout = Heat content of water drawn from the heater
Tamb = Temperature of ambient air surrounding the heater
RE = Recovery co-efficient
Pon = Rated input power
4.4
Optimization Algorithm
When installed with a real water heater, Hot water DJ
initially provides water to all the fixtures as usual. Over
time the system learns the temperature patterns and pipe
lags for each of the fixtures from the mixing ratios. Using
these learned information along with the comfort settings
(set by user through the user interface), Hot Water DJ automatically calculates an appropriate water temperature for
each of the fixtures and starts providing hot water only at
the required temperature. In order to reduce the short event
losses Hot Water DJ introduces a delay for each fixture before it starts providing hot water based on the pipe lags and
number of short events on that fixture. Therefore, Hot Water
DJ needs to define an optimization algorithm called “Maximize EnergySaved”. The optimization algorithm maximizes
the total energy saved for a given temperature and pipe lag
distribution for each faucet (in our case: kitchen sink(K),
bathroom sink(B), and shower(S)) of the home and a given
miss time. The pseudo-code for Maximize EnergySaved is
illustrated in Algorithm 4.3. Here, the calcMissTime function calculates the total miss time for the system given the
event set, nth percentile temperature, and the mth percentile
delay for the fixtures. For each of these percentile value if
the total miss time is less than or equal to the given miss
time, the energy saved for that temperature and delay is calculated using the calcEnergySaved function. calcMissTime
uses the Equation 4 to calculate the total miss time whereas
calcEnergySaved uses the Equations 5 and 6 to calculate the
total energy consumptions. From all these calculated values
the algorithm returns the value for which the energy saving
is maximized.
5
Experimental setup
In this paper, we have modeled and analyzed the data collected over 6 days in our test home as described in Section
3 to compare the performance of our Hot Water DJ with that
of a standard water heater. In order to evaluate the performance of Hot Water DJ we calculated the miss time and energy consumption of both the standard water heater and our
Hot Water DJ.
5.1
Calculation of miss time for standard water heater
In our study, we used a state-of the art tank based water
heater as our base-line. The energy factor of the heater is
0.93 which means the heating element of the heater is very
efficient. The set point temperature of the heater was 1200 F,
which means the heater was providing 1200 water for each
of the fixtures. This is why, to compute the miss times for
the standard water heater we used,
tK = tB = tS = 120
and calculated the miss times for each of the fixture using
Equation 4, 1, 2 and 3. For each of these cases AddDelay is
set to zero.
5.2
Calculation of energy consumption for
standard water heater
We used the Equation 5, 6 and 7 to compute the energy
consumption for the standard water heater. For our calculation we assumed both Thot and Ttank to be 1200 F and during
our study, the average Tamb was 750 F and the average Ti n was
800 F. The EF and RE for the water heater was 0.93 and 0.98
respectively. Pon for our heater was 4500watts. The volume
of water for each event was computed using the following
equation:
voli = f hi × duri
5.3
Calculation of miss time for Hot Water DJ
Hot Water DJ automatically selects a temperature and a
delay for each fixture of the home given a preset comfort
setting by the user. These parameters are calculated using
the optimization algorithm described in Section 4.4. With all
these parameters tK , dlK , tB , dlB , tS , and dlS for the kitchen
sink, bathroom sink and shower respectively being calculated, we used the Equation 1, 2, 3, and 4 to compute the
miss times for each of these fixtures. The total miss time of
the system would then be calculated as the sum of the miss
times of the fixtures.
5.4
Calculation of energy consumption for
Hot Water DJ
In our analysis, we assumed that our Hot Water DJ is not
going to change the hot water usage pattern of the households, which means the Hot Water DJ only saves energy as it
fills the pipe with water at lower temperature. Thus, to compute the energy consumption for the Hot Water DJ we split
each water event into two parts: the first part is for the pipe
lag and the second part is the actual hot water usage. For any
event i, we calculate the volume of water during pipe lag using Equation 8 and the volume of water during actual usage
period using Equation 9. For the Hot Water DJ, only during the pipe lag user is provided with less hot water (as Hot
Water DJ mixes some cold water straight way), so the energy
consumption during the pipe lag would be computed as if hot
water is provided at a lower temperature that is: Thot = tF for
fixture F. For the rest of the duration the energy consumption
would be as it was in the standard water heater.
6
voli−lag = f hi × lagi
(8)
voli−usage = f hi × (duri − lagi )
(9)
Results
From the first phase of our experiment, we calculated the
effects of different losses which are associated with a typical water heater. Figure 5 shows the losses calculated for the
water heater in our test home over 6 days. We used Equation
6 and 7 to calculate the stand-by energy of the heater. We
used the lag time to compute the volume of water logged inside the pipe and used that volume in Equation 5 to get the
pipe loss. Finally, we calculated short event loss as the energy consumed for the events where hot water did not reach
the faucet head. The figure shows that pipe loss accounts
for 20% of total water heater energy, compared to 11% for
standby energy. If short events are included, total pipe loss
accounts for 24% of total energy. Unless more efficient heating mechanisms are invented, the active heating energy cannot be reduced. Therefore, improved hot water heater efficiency will be achieved primarily by reducing standby energy or pipe loss energy.
Figure 5. Impact of losses associated with current hot
water heaters
For the second part of our experiment we applied the
Maximize EnergySaved algorithm on the 6 days data trace
available. The result is the Pareto optimal curve of energy
savings: the highest possible energy saved for every possible miss time. These curves are illustrated in Figure 6(a).
The miss time knob allows each user to dial in to any point
on this Pareto optimal curve. From the figure we can observe that even without adding any additional delay we can
save more than 10% of energy using Hot Water DJ. From the
figure, we can also observe that most of the energy is actually saved due to the variation in fixture temperature. This
is because, changing the fixture temperature saves energy by
reducing the pipe loss, whereas changing delay saves energy
only from the short events.
Figure 6(b) shows the breakdown of energy savings for
each fixture for a given miss time. The figure illustrates that,
if anyone wants to save a small energy without sacrificing
much of a miss time they would save from the bathroom sink,
where as if anyone is willing to allow an additional 2 mins
of miss time per day they can save more than 10% only from
the kitchen sink.
The total amount of energy saved for a given fixture
depends on its temperature/lag profile. For example, the
amount of savings that can be achieved due to temperature
control increases with pipe lag because more heat is lost in
the pipes. Since kitchen sink has the most lag times, the
kitchen sink saves the most energy through temperature control. Also, the amount of savings that can be achieved due to
delay depends on the duration of event use. That’s why delay will save the most for bathroom sink events. This is because, when hot water is used in the shower or kitchen sink,
it is often necessary for the function at hand, whereas in the
bathroom sink it is often simply for comfort while washing
hands.
7
Limitations
Our study is subject to some limitations: our current analysis is limited to one water fixture at a time. It would require a system such as WaterSense [15] or NAWMS [10] to
Figure 6. Pareto optimization curve: (a) the energy savings that can be attained for a given miss time. (b) fixture level
breakdown in energy savings
get per-fixture dis-aggregation, if simultaneous use of multiple fixtures at a time were necessary. Also, savings from
system-based temperature control makes it more difficult to
ensure hot water on the rare occasion that people want water
at a much higher temp than usual. In the interest of energy
savings, this system may cause water waste if people end up
waiting more time for hot water. Water waste also causes energy waste since clean water takes energy to purify. In future
analysis, this energy will be included in the calculations of
energy savings.
8
Conclusions
In this paper, we present a Hot Water DJ which can intelligently select water temperature for each fixture using previous fixture usage history. Hot Water DJ also intelligently introduces a fixture based delay before starting to provide hot
water by predicting short events. Our approach shows significant promise in an exploratory study carried out in a real
home for 6 days, and our calculation shows that Hot Water
DJ can attain up to 18% of energy savings by choosing the
fixture temperature and delay intelligently. In the future, we
expect to build upon the existing approach by performing an
extensive evaluation of our system that includes combining
our system with NAWMS [10] or WaterSense [15] to address
potential challenges posed due to simultaneous fixture use.
We also intend to instrument more houses and analyze the
water usage patterns and evaluate our Hot Water DJ in those
scenarios. A major concern for tank based water heaters is
the stand-by energy, in the future we also expect to address
this problem too. With these additions, we expect our Hot
Water DJ to be a viable solution for users to conserve significant amount of energy and thus reduce their electricity
bills.
9
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