SBIR Phase I: Low-Cost, Vision-Enhanced, High-Efficiency Heat Cable Control System

SBIR 第一阶段：低成本、视觉增强、高效热电缆控制系统

• 批准号: 2224907
• 负责人: Thomas Clardy
• 金额: $ 27.49万
• 依托单位: POWDER WATTS, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224907&HistoricalAwards=false
• 关键词: SBIR; Phase; Low; Cost; Vision

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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将通过开发视觉增强型加热电缆智能控制来实现。北美数以百万计的屋顶上安装了热电缆，以防止形成破坏屋顶的冰坝，但目前它们的控制措施有缺陷、简陋，并且消耗大量能源（是冬季典型家庭能耗的三倍） ）。机器学习系统结合来自易于安装的屋顶摄像头、温度传感器和当地天气预报的信息，仅在需要时打开加热电缆。北美屋顶和排水沟安装了总共 80 亿英尺的伴热电缆，每年消耗 135 太瓦时的电力，并排放 52 兆吨的二氧化碳和甲烷。初步数据表明，这种消耗、相关成本以及二氧化碳和甲烷排放量可以显着减少，对住宅和商业建筑业主产生巨大的商业影响，为客户提供一个冬季的投资回收期，并大幅减少国家的碳足迹。由于伴热电缆耗电量大，该技术还将为电力公司提供稳定电网和负载平衡的工具，为国家能源安全和竞争力做出贡献。该SBIR第一期项目建议寻求创新，以增强电力公司的能源效率。热电缆系统的能源效率。 该系统将包括一个能量收集系统，为安装在屋顶的基于摄像头的传感器系统提供动力，该系统使用机器视觉和机器学习，根据其主要功能（防止冰坝）精确控制屋顶热缆。令人惊讶的是，人们对最佳热缆控制知之甚少，包括温度、天气等关键输入变量以及屋顶特征（类型、角度、方向）的变化和作用。收集和分析这些数据将进一步了解最佳热电缆控制。热电缆功耗将与历史功耗和模型得出的功耗进行比较。技术经济分析将有助于微调和扩大收入模式。基于对屋顶摄像头系统电池进行涓流充电的能量收集技术将使该系统实现无绳化，易于对现有装置进行改造，并且维护成本低。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。