CAREER: A Scalable Occupant-Driven Energy Optimization System for Commercial Buildings

职业：商业建筑的可扩展的居住者驱动的能源优化系统

• 批准号: 1943396
• 负责人: Xiaofan Jiang
• 金额: $ 53.58万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943396&HistoricalAwards=false
• 关键词: CAREER; Scalable; Occupant; Driven; Energy

This project seeks to fundamentally change how people live their everyday lives towards a more environmentally responsible and sustainable future and has the potential to play a major role in reducing our reliance on fossil fuels and combating climate change. This research will help enable individuals to reduce their personal energy usage in indoor commercial building settings by providing real-time visibility into the energy cost of each action. This will enable energy-accountability in commercial buildings by providing actionable recommendations with quantifiable savings, as well as insights for both occupants and building managers so that they can act in a timely manner. These small savings can add up: even a small 1% reduction in commercial building energy consumption translates to 1.5 billion dollars of annual savings for the nation. This project will help advance research at the intersection of building-scale systems, Internet-of-Things, wearable systems, and recommender systems. Course modules developed on embedded systems, mobile computing, Internet-of-Things, and deep reinforcement learning will be used to train undergraduate and graduate students.This proposal aims to significantly reduce energy consumption in commercial buildings by examining each occupant’s unique and individualized energy usage, or “energy footprint”, and providing occupants with actionable and measurable energy-saving recommendations. The proposed system comprises of several components, including real-time sensing and actuation, building energy monitoring, indoor localization, large-scale time-series data analytics, and recommender systems. This project is organized into three research thrusts: (1) develop a digital twin model of a commercial building to simulate energy savings from human-driven actions, and research efficient algorithms for computing each occupant’s “energy footprint” in shared environments. (2) advance knowledge in deep reinforcement learning and design a recommender system to discover actions that have the best potential for saving energy while adapting to user preferences. (3) investigate effective feedback mechanisms and incentive schemes to encourage energy saving behaviors, increase recommendation quality, and improve recommendation acceptance rate.Research results, including datasets, embedded hardware designs, software code, simulators, smartphone application code, reports, presentations, and papers will be shared with the research community through the research group website and GitHub. To ensure privacy, any personally identifiable information in datasets will be removed. To encourage deployment and experimentation by others, documented open-source software and hardware will be release and maintain in the project’s GitHub repository at https://github.com/Columbia-ICSL/EnergyFootprinting for at least five years.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.

该项目旨在从根本上改变人们的日常生活方式，以实现对环境更加负责和可持续的未来，并有可能在减少我们对化石燃料的依赖和应对气候变化方面发挥重要作用。这项研究将通过提供对每个行动的能源成本的实时可见性，帮助个人减少室内商业建筑环境中的个人能源使用。这将通过提供具有可量化节约的可操作建议以及为居住者和建筑管理者提供见解，使他们能够及时采取行动，从而实现商业建筑的能源问责制。这些小的节省可以加起来：即使是商业建筑能耗减少1%，也可以为国家每年节省15亿美元。该项目将有助于推进建筑规模系统，物联网，可穿戴系统和推荐系统的交叉研究。该项目将利用嵌入式系统、移动的计算、物联网和深度强化学习等课程模块，对本科生和研究生进行培训。该项目旨在通过研究每个居住者独特的、个性化的能源使用情况（即“能源足迹”），为居住者提供可操作和可衡量的节能建议，从而大幅降低商业建筑的能源消耗。所提出的系统包括几个组件，包括实时传感和驱动，建筑物能源监测，室内定位，大规模时间序列数据分析和推荐系统。该项目分为三个研究方向：（1）开发一个商业建筑的数字孪生模型，以模拟人类驱动的行动节省能源，并研究计算共享环境中每个居住者的“能源足迹”的有效算法。(2)推进深度强化学习的知识，并设计一个推荐系统，以发现在适应用户偏好的同时具有最佳节能潜力的行动。(3)研究有效的反馈机制和激励方案，以鼓励节能行为，提高推荐质量，提高推荐接受率。研究成果，包括数据集，嵌入式硬件设计，软件代码，模拟器，智能手机应用程序代码，报告，演示文稿和论文将通过研究小组网站和GitHub与研究社区分享。为了确保隐私，数据集中的任何个人身份信息都将被删除。为了鼓励其他人进行部署和实验，记录的开源软件和硬件将在项目的GitHub存储库https://github.com/Columbia-ICSL/EnergyFootprinting中发布和维护至少五年。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

• DOI: 10.1145/3341162.3349336
• 发表时间: 2019-09
• 期刊: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
• 作者: Peter Wei;Haocong Shi;Jiaying Yang;J. Qian;Yinan Ji;Xiaofan Jiang

AI Stethoscope for Home Self-Diagnosis with AR Guidance

AR指导下的AI听诊器家庭自我诊断

• DOI: 10.1145/3560905.3568082
• 发表时间: 2022
• 期刊: SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: Hou, Kaiyuan;Xia, Stephen;Wu, Junyi;Zhao, Minghui;Bejerano, Emily;Jiang, Xiaofan
• 通讯作者: Jiang, Xiaofan

A Low-Cost In-situ System for Continuous Multi-Person Fever Screening

用于连续多人发烧筛查的低成本原位系统

• DOI: 10.1109/ipsn54338.2022.00009
• 发表时间: 2022
• 期刊: 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN
• 作者: Hou, Kaiyuan;Liu, Yanchen;Wei, Peter;Yang, Chenye;Kang, Hengjiu;Xia, Stephen;Spada, Teresa;Rundle, Andrew;Jiang, Xiaofan
• 通讯作者: Jiang, Xiaofan

ARSteth: Enabling Home Self-Screening with AR-Assisted Intelligent Stethoscopes

ARSteth：利用 AR 辅助智能听诊器实现家庭自我筛查

• DOI: 10.1145/3583120.3586962
• 发表时间: 2023
• 期刊: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks
• 作者: Hou, Kaiyuan;Xia, Stephen;Bejerano, Emily;Wu, Junyi;Jiang, Xiaofan
• 通讯作者: Jiang, Xiaofan

A Data-driven System for City-wide Energy Footprinting and Apportionment

• DOI: 10.1145/3433639
• 发表时间: 2021-01
• 期刊: ACM Transactions on Sensor Networks (TOSN)
• 作者: Peter Wei;Xiaofan Jiang