2224907
Project Grant
Overview
Grant Description
Sbir Phase I: Low-Cost, Vision-Enhanced, High-Efficiency Heat Cable Control System -The Broader Impact/Commercial Potential of This Small Business Innovation Research (SBIR) Phase I Project Will Be Realized Through the Development of Vision-Enhanced, Smart Control for Heat Cables.
Heat Cables Are Installed on Millions of Roofs in North America to Prevent Build-Up of Roof-Damaging Ice Dams but They Currently Have Flawed, Rudimentary Controls, and Consume Large Amounts of Energy (Tripling the Energy Consumption of a Typical Home During the Winter Months).
Combining Information From Easy-To-Install Roof Cameras, Temperature Sensors, and Local Weather Forecasting, a Machine Learning System Will Turn on Heat Cables Only When Needed.
A Total of 8 Billion Installed Feet of Heat Cable on Roofs and Gutters in North America Annually Consume 135 Terawatt-Hours of Electricity and Emit 52 Megatons of Carbon Dioxide and Methane.
Preliminary Data Indicate This Consumption, the Associated Costs, and Carbon Dioxide and Methane Emissions Can Be Reduced Significantly, Creating a Large Commercial Impact for Residential and Commercial Building Owners, a Payback Period for the Customer of One Winter Season, and a Considerable Decrease of the Nation's Carbon Footprint.
Because of Heat Cables' Large Electrical Power Consumption, the Technology Will Also Provide Electrical Utility Companies With a Tool to Stabilize the Electrical Grid and Load Balance, Contributing to National Energy Security and Competitiveness.
This SBIR Phase I Project Proposes to Pursue Innovations to Enhance the Energy Efficiency of Heat Cable Systems.
This System Will Including an Energy Harvesting System to Power a Roof-Mounted, Camera-Based, Sensor System That Uses Machine-Vision and Machine-Learning to Precisely Control Roof Heat Cables Based on Their Primary Function: the Prevention of Ice Dams.
Surprisingly, Little Is Known About Optimal Heat Cable Control, Including Key Input Variables Such as Temperature, Weather and the Variability and Role of Roof Features (Type, Angle, Orientation).
Collecting and Analyzing These Data Will Further the Understanding of Optimal Heat Cable Control.
Heat Cable Power Consumption Will Be Compared to Historical and Model-Derived Power Consumption.
Technoeconomic Analysis Will Help to Fine-Tune and Scale the Revenue Model.
The Energy Harvesting Technology Based on Trickle-Charging the Roof-Based Camera System Battery Will Make the System Cordless, Easy to Retrofit to Existing Installations, and Low-Maintenance.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
Heat Cables Are Installed on Millions of Roofs in North America to Prevent Build-Up of Roof-Damaging Ice Dams but They Currently Have Flawed, Rudimentary Controls, and Consume Large Amounts of Energy (Tripling the Energy Consumption of a Typical Home During the Winter Months).
Combining Information From Easy-To-Install Roof Cameras, Temperature Sensors, and Local Weather Forecasting, a Machine Learning System Will Turn on Heat Cables Only When Needed.
A Total of 8 Billion Installed Feet of Heat Cable on Roofs and Gutters in North America Annually Consume 135 Terawatt-Hours of Electricity and Emit 52 Megatons of Carbon Dioxide and Methane.
Preliminary Data Indicate This Consumption, the Associated Costs, and Carbon Dioxide and Methane Emissions Can Be Reduced Significantly, Creating a Large Commercial Impact for Residential and Commercial Building Owners, a Payback Period for the Customer of One Winter Season, and a Considerable Decrease of the Nation's Carbon Footprint.
Because of Heat Cables' Large Electrical Power Consumption, the Technology Will Also Provide Electrical Utility Companies With a Tool to Stabilize the Electrical Grid and Load Balance, Contributing to National Energy Security and Competitiveness.
This SBIR Phase I Project Proposes to Pursue Innovations to Enhance the Energy Efficiency of Heat Cable Systems.
This System Will Including an Energy Harvesting System to Power a Roof-Mounted, Camera-Based, Sensor System That Uses Machine-Vision and Machine-Learning to Precisely Control Roof Heat Cables Based on Their Primary Function: the Prevention of Ice Dams.
Surprisingly, Little Is Known About Optimal Heat Cable Control, Including Key Input Variables Such as Temperature, Weather and the Variability and Role of Roof Features (Type, Angle, Orientation).
Collecting and Analyzing These Data Will Further the Understanding of Optimal Heat Cable Control.
Heat Cable Power Consumption Will Be Compared to Historical and Model-Derived Power Consumption.
Technoeconomic Analysis Will Help to Fine-Tune and Scale the Revenue Model.
The Energy Harvesting Technology Based on Trickle-Charging the Roof-Based Camera System Battery Will Make the System Cordless, Easy to Retrofit to Existing Installations, and Low-Maintenance.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Park City,
Utah
84098-5322
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Powder Watts was awarded
Project Grant 2224907
worth $274,922
from National Science Foundation in July 2023 with work to be completed primarily in Park City Utah United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Low-Cost, Vision-Enhanced, High-Efficiency Heat Cable Control System
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be realized through the development of vision-enhanced, smart control for heat cables. Heat cables are installed on millions of roofs in North America to prevent build-up of roof-damaging ice dams but they currently have flawed, rudimentary controls, and consume large amounts of energy (tripling the energy consumption of a typical home during the winter months). Combining information from easy-to-install roof cameras, temperature sensors, and local weather forecasting, a machine learning system will turn on heat cables only when needed. A total of 8 billion installed feet of heat cable on roofs and gutters in North America annually consume 135 Terawatt-Hours of electricity and emit 52 Megatons of carbon dioxide and methane. Preliminary data indicate this consumption, the associated costs, and carbon dioxide and methane emissions can be reduced significantly, creating a large commercial impact for residential and commercial building owners, a payback period for the customer of one winter season, and a considerable decrease of the nation's carbon footprint. Because of heat cables' large electrical power consumption, the technology will also provide electrical utility companies with a tool to stabilize the electrical grid and load balance, contributing to national energy security and competitiveness._x000D_ _x000D_ This SBIR Phase I project proposes to pursue innovations to enhance the energy efficiency of heat cable systems.This system will including an energy harvesting system to power a roof-mounted, camera-based, sensor system that uses machine-vision and machine-learning to precisely control roof heat cables based on their primary function: the prevention of ice dams. Surprisingly, little is known about optimal heat cable control, including key input variables such as temperature, weather and the variability and role of roof features (type, angle, orientation). Collecting and analyzing these data will further the understanding of optimal heat cable control. Heat cable power consumption will be compared to historical and model-derived power consumption. Technoeconomic analysis will help to fine-tune and scale the revenue model. The energy harvesting technology based on trickle-charging the roof-based camera system battery will make the system cordless, easy to retrofit to existing installations, and low-maintenance._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
EN
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 7/18/23
Period of Performance
7/1/23
Start Date
6/30/24
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2224907
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
ZRA1NLEJ7AE4
Awardee CAGE
90VC7
Performance District
01
Senators
Mike Lee
Mitt Romney
Mitt Romney
Representative
Blake Moore
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,922 | 100% |
Modified: 7/18/23