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

建筑物通风系统对居住者没有反应，根据预定的时间表运行，以满足空间中可能存在的最大人数。这就好比一辆只在最高档行驶的汽车：效率低下、不灵活、效率低下。大量的能源被浪费，而使人们在他们的办公室太冷或太热。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）的楼宇自动化系统中纳入高分辨率实时占用数据，每年可节省16%的能源，相当于约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%的建筑物符合热舒适标准，26%的建筑物符合空气质量要求，严重影响了居住者的满意度和生产力。目前市场上的占用感测产品的小阵列是笨重的，成本太高，并且倾向于集中在建筑物操作的狭窄方面。因此，他们已经实现了采用不到1%的总非住宅真实的房地产市场。该I-Corps团队正在开发一种无源传感系统，以解决这个行业范围内的盲点，并提供一种具有成本效益且易于部署的系统，以提高建筑物的能源效率并改善居住者的舒适度。该团队正在开发一种分布式传感解决方案，使建筑HVAC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在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，这些传感器将能够在大约600平方英尺的开放空间内检测静止和移动的人。在项目结束时，该团队打算完成图像处理部分的软件原型，该软件原型能够使用样本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，从高级占用数据中探索能源效率和舒适度之外的价值主张，并确定了我们应该关注的真实的房地产客户类型。所提出的产品有可能大大降低建筑能耗，居住者的舒适度在整个美国，更广泛地说，使建筑环境更加响应和数据驱动。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，从而将计算机视觉和机器学习研究与开发的应用推向市场。

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.