II-New: A Programmable Data-Driven Testbed for Sustainable Buildings Research

II-新：用于可持续建筑研究的可编程数据驱动测试台

• 批准号: 1405826
• 负责人: Prashant Shenoy
• 金额: $ 58.7万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405826&HistoricalAwards=false
• 关键词: II; New; Programmable; Data; Driven

Today's buildings consume nearly 40% of the total energy, and 75% of the electricity, produced by the United States. Consequently, the use of computing, sensing, and communication to make buildings smarter and greener by reducing their energy usage, costs, and footprint has become increasingly important. Many research questions arise when using computational techniques for making buildings smarter and greener. For example, how do we ``green'' buildings by integrating renewable sources, such as solar and wind, into the building energy management system? How do we reduce energy usage by automating error-prone manual tasks, such as turning down a thermostat? Unfortunately, current research is this area faces two key barriers: lack of access to energy usage data from real buildings, and an inability to conduct experimental research in live buildings due to reasons of safety and comfort. This project focuses on alleviating such barriers by building infrastructure to collect anonymous energy usage data from real homes for research purposes, as well as building a programmable mock building to enable experimentation with smart energy management algorithms.This project will build a programmable data-driven test-bed to enable a range of research and education activities focused on sustainable buildings. Our test-bed will be comprised of two components: a data collection component to instrument a large number of real buildings of various types (e.g., single family homes, multifamily dwellings, apartments, public housing and small office buildings) to gather anonymous fine-grained actual energy usage and operational data at scale; and a programmable smart building component that comprises programmable power sources and electrical loads to enable a variety of energy management mechanisms to be evaluated in safe and repeatable manner inside a ``mock" smart building. The test-bed will enable a variety of novel research and education activities like designing intelligent load management techniques and strong user incentives for adopting such technologies; integrating renewables and energy storage into a smart building's energy management mechanisms; using learning and modeling to drive analytics of energy data to derive rich insights, as well as the design of privacy mechanisms for smart grid technologies, such as smart metering; and, using the platform for hands-on education and outreach to a broad community.Interested researchers in this field will have access to realistic data sets and a test-bed to conduct empirical research in collaboration with local electric utility. As a result, the research enabled by the test-bed has the potential to directly impact the operations and efficiency of a real utility's grid management. There is a significant community component to share anonymized data from our instrumented buildings, as well as the design specifications with the broader research community.

今天的建筑消耗了美国近 40% 的总能源和 75% 的电力。因此，利用计算、传感和通信来减少建筑物的能源使用、成本和占地面积，使建筑物变得更加智能和绿色，变得越来越重要。 当使用计算技术使建筑物变得更智能、更环保时，会出现许多研究问题。 例如，我们如何通过将太阳能和风能等可再生能源整合到建筑能源管理系统中来实现“绿色”建筑？我们如何通过自动执行容易出错的手动任务（例如关闭恒温器）来减少能源使用？ 不幸的是，目前该领域的研究面临两个关键障碍：无法获取真实建筑的能源使用数据，以及出于安全性和舒适性的原因无法在实际建筑中进行实验研究。 该项目的重点是通过构建基础设施来从真实家庭收集匿名能源使用数据以用于研究目的，以及构建可编程模拟建筑以进行智能能源管理算法的实验，从而减轻这些障碍。该项目将构建一个可编程数据驱动的测试台，以支持一系列以可持续建筑为重点的研究和教育活动。我们的测试平台将由两个部分组成：数据收集部分，用于对大量各种类型的真实建筑（例如单户住宅、多户住宅、公寓、公共住房和小型办公楼）进行检测，以大规模收集匿名的细粒度实际能源使用和运营数据；以及由可编程电源和电力负载组成的可编程智能建筑组件，使各种能源管理机制能够在“模拟”智能建筑内以安全和可重复的方式进行评估。该试验台将支持各种新颖的研究和教育活动，例如设计智能负载管理技术和对采用此类技术的强烈用户激励；将可再生能源和能源存储集成到智能建筑的能源管理机制中；利用学习和 建模以驱动能源数据分析以获得丰富的见解，以及智能电网技术（例如智能电表）的隐私机制的设计；并利用该平台进行实践教育并推广到更广泛的社区。对该领域感兴趣的研究人员将可以获得真实的数据集和试验台，以便与当地电力公司合作进行实证研究。 因此，试验台进行的研究有可能直接影响 真实公用事业电网管理的运营和效率。有一个重要的社区组件可以与更广泛的研究社区共享来自我们的仪表化建筑的匿名数据以及设计规范。

项目成果

Enforcing fair grid energy access for controllable distributed solar capacity

为可控分布式太阳能容量实施公平的电网能源接入

• DOI: 10.1145/3137133.3137147
• 发表时间: 2017
• 期刊: ACM International Conference on Systems for Energy-Efficient Built Environments
• 作者: Bashir, Noman;Irwin, David;Shenoy, Prashant;Taneja, Jay
• 通讯作者: Taneja, Jay

vSolar: Virtualizing Community Solar and Storage for Energy Sharing

vSolar：虚拟化社区太阳能和存储以实现能源共享

• DOI: 10.1145/3208903.3208932
• 发表时间: 2018
• 期刊: ACM International Conference on Future Energy Systems
• 作者: Lee, Stephen;Shenoy, Prashant;Ramamritham, Krithi;Irwin, David
• 通讯作者: Irwin, David

DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery

• DOI: 10.1145/3292500.3330741
• 发表时间: 2019-07
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Stephen Lee;Srinivasan Iyengar;Menghong Feng;Prashant J. Shenoy;Subhransu Maji

Helios: a programmable software-defined solar module

Helios：可编程软件定义太阳能模块

• DOI: 10.1145/3276774.3276783
• 发表时间: 2018
• 期刊: Proceedings of the 5th Conference on Systems for Built Environments
• 作者: Bashir, Noman;Irwin, David;Shenoy, Prashant
• 通讯作者: Shenoy, Prashant

Weatherman: Exposing weather-based privacy threats in big energy data

气象员：揭露大能源数据中基于天气的隐私威胁

• DOI: 10.1109/bigdata.2017.8258032
• 发表时间: 2017
• 期刊: IEEE International Conference on Big Data
• 作者: Chen, Dong;Irwin, David
• 通讯作者: Irwin, David