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

建筑通风系统对居住者没有反应，按照预定的时间表运行，以满足空间中可能容纳的最大人数。这与一辆只以最高档位行驶的汽车相提并论：低效、僵硬、低效。当人们在办公室里太冷或太热时，大量的能量被浪费了。根据太平洋西北国家实验室进行的一项研究，通过在控制供暖、通风和空调(HVAC)的建筑自动化系统中整合高分辨率实时入住率数据，每年大约相当于27亿美元，有可能节省16%的能源。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%的建筑达到了热舒适标准，26%的建筑达到了空气质量要求，严重影响了居住者的满意度和生产率。目前市场上为数不多的入住率感应产品笨重、成本太高，而且往往专注于建筑运营的狭隘方面。因此，它们在整个非住宅房地产市场中的占有率不到1%。I-Corps团队正在研究一种被动传感系统，以解决这一行业范围内的盲点，以经济高效且易于部署的系统来提高建筑能效和改善居住舒适性。该团队正在研究一种分布式传感解决方案，该解决方案将使建筑暖通空调控制系统能够响应并预测建筑物的占用情况。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，这些传感器处理来自低成本RGB摄像头和被动红外传感器的数据，该传感器将能够在开放空间的约600平方英尺覆盖区域内检测静止和移动的人。到项目结束时，该小组打算完成图像处理部分的软件原型，能够使用样本建筑图像流提供实时入住率统计和预测。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，从先进的入住率数据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类型。拟议中的产品有可能在全美大幅降低建筑能耗并改善居住者的舒适性，更广泛地说，使建筑环境变得更加灵敏和数据驱动。该团队在i-Corps项目过程中的采访将对塑造将计算机视觉和机器学习研究和开发的这种应用推向市场的商业模式至关重要。

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.