I-Corps: Conceptualizing and Validating an Occupant-aware Predictive Control System

I-Corps：概念化和验证乘员感知预测控制系统

• 批准号: 1639266
• 负责人: John Taylor
• 金额: $ 5万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639266&HistoricalAwards=false
• 关键词: Corps; Conceptualizing; Validating; Occupant; aware

Building ventilation systems are not responsive to occupants, operating according to predetermined schedules to satisfy the maximum number of people that could be in a space. It's comparable to a car that only runs in the highest gear: ineffective, inflexible, and inefficient. Vast amounts of energy are wasted while making people too cold or hot in their offices. Based on a study conducted by Pacific Northwest National Labs there is a potential for 16% energy savings through incorporation of high resolution real-time occupancy data in building automation systems that control heating, ventilation and air conditioning (HVAC) equating to roughly $2.7 billion annually. A survey conducted by Center for Built Environment at UC Berkeley showed that merely 11% of buildings fulfill standards for thermal comfort, and 26% meet air quality requirements, severely impairing occupant satisfaction and productivity. The small array of occupancy sensing products currently on the market are cumbersome, too costly, and tend to focus on narrow aspects of building operation. As a result they've achieved adoption of less than 1% of the total nonresidential real estate market. This I-Corps team is working on a passive sensing system to address this industry-wide blind spot with a cost-effective and easy to deploy system in order to increase building energy efficiency and improve occupant comfort.This team is working on a distributed sensing solution that will enable building HVAC control systems to respond to and anticipate building occupancy. The complete initial version of the system will include machine learning and computer vision algorithms embedded in the small fully wireless sensors processing data from a low-cost RGB camera and passive infrared sensor that will be able to detect both stationary and moving people across a coverage area of approximately 600 square feet in an open space. By the end of the program the team intends to have completed a software prototype for the image processing portion capable of delivering real-time occupancy counting and prediction using sample building image streams. More importantly, the team plans to have validated the demand in the market for the proposed product through the interviews, investigated the potential privacy concerns from prospective clients, explored value propositions outside of energy efficiency and comfort from advanced occupancy data, and identified the types of real estate clients that we should focus on. The proposed product has the potential to greatly reduce building energy consumption and improve occupant comfort throughout the US, and more broadly make the built environment far more responsive and data-driven. The team's interviews over the course of I-Corps program will be vital in shaping the business model to bring this application of computer vision and machine learning research and development to market.

建筑通风系统不会对居住者做出反应，而是根据预定的时间表运行以满足空间内可能存在的最大人数。它就像一辆只以最高档行驶的汽车：低效、不灵活、低效。当人们在办公室里过冷或过热时，会浪费大量的能源。根据太平洋西北国家实验室进行的一项研究，通过将高分辨率实时占用数据纳入控制供暖、通风和空调 (HVAC) 的楼宇自动化系统，可以节省 16% 的能源，相当于每年约 27 亿美元。加州大学伯克利分校建筑环境中心进行的一项调查显示，只有 11% 的建筑满足热舒适度标准，26% 的建筑满足空气质量要求，严重影响了居住者的满意度和生产力。目前市场上的少量占用感应产品很笨重、成本太高，并且往往集中在建筑运营的狭窄方面。因此，他们的采用率仅占非住宅房地产市场总量的不到 1%。 I-Corps 团队正在开发一种无源传感系统，通过经济高效且易于部署的系统来解决整个行业的盲点，从而提高建筑能源效率并改善居住者的舒适度。该团队正在开发分布式传感系统该解决方案将使建筑 HVAC 控制系统能够响应并预测建筑占用情况。该系统的完整初始版本将包括嵌入小型全无线传感器中的机器学习和计算机视觉算法，处理来自低成本 RGB 摄像头和被动红外传感器的数据，这些传感器将能够检测覆盖区域内静止和移动的人员约 600 平方英尺的开放空间。在项目结束时，团队打算完成图像处理部分的软件原型，能够使用示例建筑图像流提供实时占用计数和预测。更重要的是，该团队计划通过访谈验证市场对拟议产品的需求，调查潜在客户的潜在隐私问题，从先进的占用数据中探索能源效率和舒适度之外的价值主张，并确定我们应该关注的房地产客户。拟议的产品有可能大大降低整个美国的建筑能源消耗并提高居住者的舒适度，并更广泛地使建筑环境更加灵敏和数据驱动。该团队在 I-Corps 项目过程中的采访对于塑造商业模式至关重要，以便将计算机视觉和机器学习研究与开发的应用推向市场。