Good eveningMy name is Eric Larson – I am a PhD student in Engineering from the University of WashingtonMy aim with this talk is two foldI want to inform you about this technology and what it can doBut I also want your feedback for how you think you can use this technologyI want to know what your concerns are so that we can better fit this technology to your application
…that water efficiency starts in he home. Whether it be
Such that I could tell you 11% of all the water used in the home came from the downstairs shower?Even more than that what if this technology could tell you exactly how much of that water was hot and how much was cold, so that I could also tell you higher level information like 22% of all the hot water in the home comes from the downstairs bathroom.Having this type of end use information could be invaluable, but the way in which it is sensed is equally important. This begs the question:
How can you sense this? When we started this project we were in search of a technology that could disaggregate water use down to the valve level – this is not new, we are in the end uses forum of the conference But there were other major hurdles that prevent this type of technology from having a significant impact. Namely, Homeowner can install
Could we leverage existing water meters? Not really. They only provide an aggregate amount of the total water entering the home and cannot be read automatically.AMR meters - wirelessly read meters or smart meters– solve this problem by making flow data available but it is unclear how often the aggregate flow data will be available – every day vs. every fifteen minutes, to disaggrgate water, we would need to sample the water flow much more often, say every second. However, to disaggregate hot water you would need to submeter water flowing from the hot water heater – requiring a trained professional, which violates our easy to install requirement
What about a more indirect approach? In the end we decided to use fluctuations in water pressure in the home to infer activity.Our system, called hydrosense, is a single pressure sensor that connects to a wireless transmitter. It enables us to view the water pressure in a home in real time. To explain how this works we need to briefly talk about typical plumbing systems.Water typically enters a home at a regulated 45 to 90 psi. The pressure in your home is a closed system. Everything is connected to everything else through the plumbing system.
So a single pressure sensor in a home take advantage of the existing infrastructure to do all the work. When you turn on or off the cold water in …
Since the video was difficult to see, I have graphed the signal here, again this is pressure versus timeThere are three things to notice from this graphThere is a pressure wave telling when the event started, when the event endedAnd a sustained pressure drop while water is running. The magnitude of that pressure drop is directly related to the amount of water flowing from the valve. After a simple calibration, we can use the pressure entirely to say how long the water was running and the exact flow rate,Everything we need to calculate the volume of water escapingThe instant a valve is opened or closed (be it a bathroom faucet or a mechanical valve in a dishwasher), a pressure change occurs and a pressure wave is generated in the home plumbing system. The transient can have a positive or negative rate of change depending on whether a valve is being opened or closed.The pressure drop is indicative of how much water is being used.
CalibrationMultiple sensing points
To validate our approach we conducted staged events where two researchers activated water fixtures in different homes, giving several examplesI will refer those interested in details to our paper, for now I just want to iterate that we tested our system using staged experiments in ten different residences, including an apartment complex, and a cabin on a private well – for a grand total of 706 trials and 84 water fixtures
There are a few ways to view how accurate hydrosense is at disaggregating water usage – for fixture level disaggregation (toilet vs bathroom sink vs kitchen sink) we can identify water usage with 96% accuracy. For individual hot and cold valves inside those fixtures, our classification accuracy only drops slightly, 94% accuracy. We can also break down the results in other ways, such as looking at classifying the open or close pressure waves, and breaking down by fixtureThe results are about the same. Every fixture, whether a valve open water hammer wave or or valve close water hammer wave, can be identified with high accuracy.
This figure shows the accuracy of fixture-level identification of valve open and valve close events within each home as well as the aggregate 97.9% accuracy of fixture-level classification.Many homes resulted in 100% open and close fixture classification.The worst performing house, H10, was due to noise from the eleven cabins that share the same supply line at the resort.
So now we know a little more about the technology – a reliable, easily installed, retrofit solution for disaggregated sensing I would like to share with you what we have hypothesized this technology could be used for
The new water bill could provide disaggregated breakdown of water usage to the consumer and we can start to bride the disconnect. We can suggest water practices tailored to individual useOr offer web services like this that offer the consumer a way to easily track their water usage in real time and make water saving suggestions like based on your daily usage, installing this low flow fixture would save you X amount on your water bill and Y in heating bill, paying for itself in Z months.
Track and fine inappropriate water userealtime demand trackingSo that tiered pricing models can be based not only on when water is use, but also how it is used – so we know if someone is watering their lawn in the middle of the day.Or maybe tracking leaks, running toilets, leaky flapper valves in real time
exactly measure how much water is saved in a low flow fixture by consumers, in addition to increased market opportunity from web servicesRate of return for financial and tax incentives aimed at conserving waterreal world water usage performance and efficacy of fixturesoperating cost in different homes focus of new manufacturing techniquesend-use regulationShower head
commercial business like grocery stores and cafes can use this to pinpoint water inefficiencies and inform future policiesWe have concentrated on residential water use but there are significant savings to be had in industrial and commercial settings – This technology could help companies reach that - Triple bottom linelike the Starbucks examplepinpoint deficiency issues Leaks – separate slides
So we have hypothesized that this system could enable a number of different applications. And we are currently researching the challenges and solutions associated with sub-metering in apartment complexes, we are at the end of a three month longitudinal deployment in five Seattle homes, I mentioned that we use pressure drops to calculate flow - we are also working on ways to piggy back hydrosense into the advanced metering initiative so that we can have water flow without this calibration step, and in the grand scheme of efficiency we have built a prototype system that runs batteryless – harvesting the power needed to run from the water hammer wave itself.
To do this, we’ve built a range of small, lightweight sensors to distribute around the home to collect ground truth data on water fixture usage.
But we also want to know what other avenues you would be interested in seeing this system takeShould we be eliminating calibration, currently we can tell if a water hose is running, but should we be concentrating on different outdoor water usage – like telling a sprinkler from a hand sprayer?, or making this more adaptable to large industrial and commercial buildings like schools and offices?, or maybe we should look more in-depthly at leaks
BuildingsGround truth sensors
Flow rate is related to pressure change via Poiseuille’s Law, which states that the volumetric flow rate of fluid in a pipeQ is dependent on the radius of the pipe r, the length of the pipe L, the viscosity of the fluid μ and the pressure drop ΔP.This can be simplified using the fluid resistance formulation, which states that the resistance of flow is proportional to the drop in pressure divided by the volumetric flow rate.(click)Simple substitution leads to: flow rate = change in pressure / fluid resistance
So, we know the change in pressure because that’s what HydroSense measures but we want to know flow rate. To do that, we have to find values for the fluid resistance, which differ according to fixture and fixture location.---This is analogous to ohms law, which relates voltage, resistance, and current.HydroSense measures the change in pressure ΔP. However, in order to estimate flow rate we must know Rf.
One way to do this would be to measure Rf at every valve in the home and save this in a dictionary. Then, the fixture classification algorithms would look up this value and solve for Q.---To acquire Rf, one could measurethe flow rate (Q) at each individual valve as well as the corresponding pressure change (∆P) at the hydrosense installation location. The division of these two values results in Rf.We can then use the learned Rf values for each valve to estimate flow during an automatically detected water usage event.
For our flow estimate results, three of four houses tested (H1, H4, H5) have error rates below 8% (or approximately 0.16 GPM**), comparable to 10% error rates found in empirical studies of traditional utility-supplied water meters . (click)The fourth house (H7), however, had an error rate above 20%. We believe this isdue to the installation location of the sensor. Whereas the first three homes had HydroSense installed on an exterior water bib, H7’s installation used the hot water heater drain valve.This results in two confounding pressure sources (the supply water main and the gravitational pressure of the water in the tank). It seems the cold water valves were particularly affected.
When the cold water valves are removed, H7 flow estimation results are on par with the other homes.
Here we have flow data using interpolated Rf. The Y-Axis is the average error in GPM and the x-axis is the number of random Rf samples needed to obtain those error values. As you can see, after randomly selecting five Rf values, the average error dropped 74% to 0.27 GPM.
We have been working lately on a 2.0 version of HydroSense, which is focused primarily on naturalistic water usage.
HydroSense for Water Management Scientists
Eric Larson<br />MSEE, PhD Candidate<br />University of Washington<br />ACEEE Hot Water Forum<br />May 13, 2010<br />design:<br />use:<br />ubicomp lab<br />build:<br />university of washington<br />university of washington<br />
water efficiency starts in the home<br />water tower<br />bathroom 1<br />hose<br />spigot<br />kitchen<br />thermal <br />expansion <br />tank<br />hot <br />water heater<br />bathroom 2<br />
what about where water goes?<br />water tower<br />bathroom 1<br />22%<br />hose<br />spigot<br />kitchen<br />thermal <br />expansion <br />tank<br />dishwasher<br />11%<br />hot <br />water heater<br />bathroom 2<br />laundry<br />
how to<br />sense?<br />valve level <br /> disaggregation<br />easy to install<br /> low-cost<br /> reliable<br />
water meters?<br />typical water meters<br /><ul><li> only provide aggregate information on water usage
require pipe modification for installation</li></ul>AMR<br />