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

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

Md Anindya Prodhan

Department of Computer Science University of Virginia

mtp5cx@virginia.edu

Kamin Whitehouse

Department of Computer Science University of Virginia

whitehouse@virginia.edu

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 mon-itoring systems to create the Hot Water DJ, which provides hot water to any fixture based on the requirement of the fix-ture. 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 lim-ited impact on user comfort and cost.

Categories and Subject Descriptors

C.3 [Special-Purpose and Application-Based Sys-tems]: Real-time and Embeded Systems

; H.1.2 [Models and Principles]: User/Machine Sys-tems—Human Information Processing

General Terms

Design, Experimentation, Economics, Human Factors

Keywords

Water heater, Energy consumption, Mixer, Wireless sen-sors, 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 na-tional energy budget which is equivalent to making jet fuel

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10% more efficient. Therefore, making water heaters effi-cient is an important problem to address.

Most of the homes in the U.S. typically use a single wa-ter heawa-ter for the whole house which is either a tank based water heater or a tankless water heater. Tank based wa-ter heawa-ters keep hot wawa-ter 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 calledpipe lossand studies show it to account for more than 20% of water heating energy in the typical home [12]. Today, the main solution to this prob-lem 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 consid-ered mechanical changes to improve the efficiency of the water heaters. However, in this project we view energy effi-ciency of water heaters from a computational point of view.

In this project, we propose aHot 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 ev-ery fixture requires different water temperatures on average, and that each fixture is subject to different pipe lag depend-ing 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 wa-ter for such events is entirely wasted. Upon further investi-gation, 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 aHot 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.

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 heat-ing energy with limited effect on user comfort.

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 market-ing benefit of bemarket-ing “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 adop-tion 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 solu-tions 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 perfor-mance evaluation of our Hot Water DJ is presented in Sec-tion 6. SecSec-tion 7 points out some limitaSec-tions of our study. We conclude with a brief summary in Section 8.

2

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 re-search 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 consump-tion.

2.1

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 en-ergy 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, point-of-use water heaters are generally used for one or two fix-tures 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 lossis defined as the energy re-quired 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 expen-sive 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 wa-ter heawa-ters share a common problem calledpipe loss. When-ever 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 calledpipe 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 es-timate 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 typ-ical 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 disag-gregate total flow measured at a central location in an unsu-pervised 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] Srini-vasan 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 sen-sor and temperature sensen-sors. Water flow meters were instru-mented 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 result-ing flow of water through the pipe. The flow meter reports instantaneous water flow (in cubic meters per hour) at a fre-quency of 2Hz using the homes Wifi connection to transmit data. We used z-wave sensors and emonitor sensors to iden-tify water fixtures for any water event. In our test home, we used aeon lab’s z-wave sensors [8] which detect every fix-ture on/off event and notify them wirelessly to a z-wave con-troller 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 signa-tures of these devices to identify when they are turned on/off. We also used temperature sensors to measure the tempera-ture of the water coming out of the fixtempera-ture. These temper-ature sensors are used as a ground truth for water tempera-ture and pipe lag calculation. We instrumented each fixtempera-ture with a LM35DZ [4] temperature sensor from National Semi-conductor Corporation. These sensors are used to measure the instantaneous water temperature in 0C 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 ar-rived at three new observations:

3.1

Observation I

The first observation is: every fixture requires different temperature water. This is because people use different tem-perature 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 typi-cal home each fixture is in a different distance from the wa-ter heawa-ter due to the piping structure of the home. As a re-sult, the time required for the hot water to reach the fixture head (which we define aspipe lag) is different. Even for the same fixture the pipe lag is highly variable as often some fix-tures 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 me-ters. 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 Fig-ure 3(b) we present the ground truth temperatFig-ure 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 be-ginning when the consumer turns the shower (or bath spigot in a combined shower/bath) to full hot. This is how con-sumers 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 flow-ing water are ready, the hot water flow is reduced and some cold water is mixed in to achieve the desired showering tem-perature. 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 de-fine a threshold calledhot 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 900F and when-ever hot water is not used the temperature remains below 900F. 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 se-lects the hot water temperature intelligently for any fixture event to save energy.

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 in-crease the hot water temperature by 50F.

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 pres-sure 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

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 com-fort 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 thosendays where each elementiof the vector contains a hot water flow (f hi),

cold water flow (f ci), duration (duri), median temperature

(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 de-fine a set of temperature and a set of delays for each fixture so that maximum energy is saved without exceeding the al-lowable comfort loss.

In order to formulate the problem let us define the follow-ing 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.

Pipe lag Distribution Pipe lag distributionfor a fixture is defined as the set of all the lag times for the events as-sociated with that particular fixture.

Miss Time Miss timeis 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 mea-sure of comfort. For our system, total miss time consists of three components: temperature penalty (TempPen), pipe lag penalty (PipeLagPen) and additional delay (AddDelay). TempPenis 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 se-lected 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 throughTempPen are not penalized again.AddDelayPenis the delay Hot Water DJ introduces to deal with short events. Thus, for any event pof a fixtureF,TempPen,PipeLagPenandAddDelayPen are computed using the Equations 1, 2 and 3 respectively. Here, the selected temperature and delay for the fixture isF istF anddlF respectively. Finally, miss time is the sum of

these penalties over all the events in HE (Equation 4).

TempPenp= durp, iftmpp>tF 0, otherwise (1) PipeLagPenp= lagp, iftmpp<tF 0, otherwise (2) AddDelayPenp= ( dlF 2 , ifp∈HE 0, Otherwise (3) MissTimeF=

∑

(TempPen_p+PipeLagPen_p+AddDelayPenp)

(4) We used an energy consumption model to calculate the energy consumption for our Hot Water DJ from the wa-ter usage data trace over the 6 days and compared that with the energy consumption of a typical tank based wa-ter heawa-ter installed in our test home. Wawa-ter Heawa-ter Analysis

Algorithm 1MaximizeEnergySaved

Input: Miss Time mt, Temperature Distribution tdK, tdB,

tdS, Pipe Lag Distribution pdK, pdB, pdS, Event Set

E 1: esmax= 0 2: forn = 0 to 100do 3: form = 0 to 100do 4: tK = percentile(tdK, n) 5: tB= percentile(tdB, n) 6: tS= percentile(tdS, n)

{tK,tBandtSare the temperatures selected for the

kitchen sink, bathroom sink and shower respec-tively for the current iteration}

7: dlK= percentile(pdK, m) 8: dlB= percentile(pdB, m)

{dlKanddlBare the delays selected for the kitchen

sink and bathroom sink respectively for the current iteration} 9: ifcalcMissTime(tK,tB,tS,dlK,dlB,E)≤mtthen 10: es= calcEnergySaved(tK,tB,tS,dlK,dlB,E) 11: ifes≤esmaxthen 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 consump-tion equaconsump-tion for water heaters. Equaconsump-tion 5, Equaconsump-tion 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×Cp×(Thot−Tin)

EF (5)

Qstdby=UA×(Ttank−Tamb)×(24−

Qout RE×Pon ) (6) UA= 1 RE− 1 EF (Ttank−Tamb)×₄₁₀₉₄24 −_RE×1Pon (7) Where,Qheat= Heating energy

Qstdby= Standby energy

vol= volume of water drawn per day den= density of water

Cp= 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 au-tomatically 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 be-fore 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 “Maxi-mizeEnergySaved”. 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 MaximizeEnergySavedis illustrated in Algorithm 4.3. Here, thecalcMissTime func-tion calculates the total miss time for the system given the event set,nthpercentile temperature, and themthpercentile 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 cal-culated using thecalcEnergySavedfunction.calcMissTime uses the Equation 4 to calculate the total miss time whereas calcEnergySaveduses 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 col-lected 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 perfor-mance of Hot Water DJ we calculated the miss time and en-ergy consumption of both the standard water heater and our Hot Water DJ.

5.1

Calculation of miss time for standard

wa-ter heawa-ter

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 1200F, 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 casesAddDelayis 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 calcula-tion we assumed bothThotandTtankto be 1200F and during

our study, the averageTambwas 750F and the averageTinwas

800F. TheEFandREfor the water heater was 0.93 and 0.98 respectively. Ponfor 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 parameterstK,dlK,tB,dlB,tS, anddlSfor the kitchen

sink, bathroom sink and shower respectively being calcu-lated, 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 house-holds, which means the Hot Water DJ only saves energy as it fills the pipe with water at lower temperature. Thus, to com-pute 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 eventi, we calculate the volume of water during pipe lag us-ing Equation 8 and the volume of water durus-ing actual usage period using Equation 9. For the Hot Water DJ, only dur-ing 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=tFfor

fixture F. For the rest of the duration the energy consumption would be as it was in the standard water heater.

voli−lag= f hi×lagi (8)

voli−usage= f hi×(duri−lagi) (9)

6

Results

From the first phase of our experiment, we calculated the effects of different losses which are associated with a typi-cal water heater. Figure 5 shows the losses typi-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 in-side the pipe and used that volume in Equation 5 to get the pipe loss. Finally, we calculated short event loss as the en-ergy 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 heat-ing mechanisms are invented, the active heatheat-ing energy can-not be reduced. Therefore, improved hot water heater effi-ciency will be achieved primarily by reducing standby en-ergy or pipe loss enen-ergy.

Figure 5. Impact of losses associated with current hot water heaters

For the second part of our experiment we applied the MaximizeEnergySaved 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 possi-ble 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 ob-serve 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 actu-ally 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 con-trol. Also, the amount of savings that can be achieved due to delay depends on the duration of event use. That’s why de-lay will save the most for bathroom sink events. This is be-cause, 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 anal-ysis is limited to one water fixture at a time. It would re-quire 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 mul-tiple 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 en-ergy waste since clean water takes enen-ergy 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 intel-ligently select water temperature for each fixture using previ-ous fixture usage history. Hot Water DJ also intelligently in-troduces a fixture based delay before starting to provide hot water by predicting short events. Our approach shows sig-nificant 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 sig-nificant amount of energy and thus reduce their electricity bills.

9

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