| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | December 8, 2014 |
| Latest Amendment Date: | December 8, 2014 |
| Award Number: | 1448573 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Benaiah Schrag
bschrag@nsf.gov (703)292-8323 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | January 1, 2015 |
| End Date: | June 30, 2015 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
137 Varick St New York NY US 10013-9998 (814)883-6687 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
137 Varick St. New York NY US 10013-9998 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase I |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
This Small Business Innovation Research phase I project will investigate a wireless human presence detection (WHPD) system that makes use of extant metadata to enable devices - in the near term, vents controlled by short-range wireless networks - to program themselves and respond to changes in users' habits and environment without the addition of any new hardware. A vent that can build a predictive schedule can close when a user is not likely to be present. For example, people rarely use their kitchen or den when sleeping at night. Since closing these vents intelligently has been shown to save between 14% and 30% of a heating, ventilation, and air conditioning (HVAC) system's run-time, a way to automatically close them will save money, energy, and improve user comfort. Per the 2009 U.S. Census, there are 97 million American homes with a central HVAC system and 61% of all American households have a home Wi-Fi network. With an average of four rooms over-conditioned at various times, there are at least 160 million rooms at suboptimal temperatures. Based on these figures, and a sub $100 retail price for such a device, the U.S. market size is roughly $13 billion.
The intellectual merit of this project will hinge on the capability to turn extant wireless metadata into effective wireless human presence detection (WHPD) as an enabling technology capable of enhancing many different technologies already used in home/commercial/industrial automation. Current presence-detecting methodologies usually require motion detection to function. There are presence detectors that identify people by their thermal, or visual light shapes (object recognition), but these devices are unfit for consumer applications due to cost and complexity. This project will result in an algorithm to detect presence reliably, or semi-reliably, from received signal strength indicator (RSSI) or any other method that does not require specialized hardware. The broader impact/commercial potential of a wireless presence detector enables many more technologies as well. For instance, the Philips Hue light bulb is a device on its own ZigBee powered network. When integrated with the proposed WHPD system, the Hue could function as a motion sensing light, turning itself off when no one is in the room. Similarly, any connected thermostat could become a learning thermostat, using the WHPD data supplied by this system as a way to improve home heating and cooling efficiency.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
With the help of the NSF, Keen Home has successfully created and tested an internet connected Smart Vent and automation backend. This pair of technologies allows control of temperature in every room of a house. Current HVAC systems rely on a thermostat as a single point of control for temperature in all rooms at once. This is like having a single light switch to control all of our lights at once. The Smart Vent senses temperature and pressure and can make decisions about vent aperture to redirect air to the rooms that need it, eliminating hot and cold spots and waste.
The project focused specifically on in-house (real world) design and testing. First, basic HVAC safety was established and ensured. Next, a complex set of instruments was used to determine under what conditions room temperatures could be affected. It was found that rooms can be varied by up to 10 degrees Fahrenheit even in underperforming HVAC systems. Finally, it was demonstrated that given proper control algorithms, the Smart Vent can be used to reduce HVAC run time and therefor energy usage.
This data was used to create a robust computer control algorithm and backend infrastructure that can be improved over time. Ultimately, this information will become the groundwork of ongoing research intended to allow the vent to be operated like a thermostat in every room (where temperature can be set, and automatically controlled). This is the focus of our NSF Phase II proposal.
Last Modified: 09/01/2015
Modified by: Will J Mcleod
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