PFI:BIC - Utility-driven Smart Energy Services

PFI：BIC - 公用事业驱动的智能能源服务

• 批准号: 1534080
• 负责人: Prashant Shenoy
• 金额: $ 100万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534080&HistoricalAwards=false
• 关键词: PFI; BIC; Utility; driven; Smart

This Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project focuses on the development of a utility-driven energy service platform. Since buildings consume a large fraction of society's total energy usage, even modest improvements in building energy efficiency have the potential to yield significant benefits. In recent years, utilities have deployed tens of millions of smart electric meters that record building energy usage over short intervals (e.g., every few minutes). While the original purpose of smart meters was to support basic utility operations (e.g., automated meter reading), this project uses them as the foundation for developing a new class of smart energy service systems. Specifically, the project focuses on analyzing the vast amount of data available from utility smart meters and other networked sensors to improve the energy efficiency of buildings and the electric grid. The research is utility-driven, since utilities have i) access to massive amounts of customer energy data, ii) a way to deliver the results of analytics to customers, and iii) strong incentives to improve customer energy efficiency (e.g., by reducing peak demand to reduce their own operational costs). The approach is scalable, since it leverages already available building smart meter data, rather than requiring the installation of new smart devices and control systems. The project will develop utility-driven smart energy services to improve grid energy efficiency, encourage energy conservation, and promote the local renewable energy sources, such as rooftop solar. The research methodology includes developing new energy analytic techniques for smart meter data, combining these in novel ways to create specific applications that improve energy efficiency, packaging those applications as cloud-based web services that are accessible to end users, and evaluating their impact on user behavior and energy efficiency. In particular, the project will develop three specific smart energy service systems: i) iProgram to determine optimal thermostat schedules by analyzing smart meter data, ii) Green Demand Response (Green-DR) to opportunistically schedule elastic loads to align with renewable energy generation, and iii) PowerTrip to provide real-time energy event notifications and suggested conservation actions to users. The project will implement a prototype of each system and evaluate them on real-world data (rendered anonymous) from 18,000 smart meters. The research follows a human-in-the-loop model that integrates technical and user behavioral research in the design and implementation of smart energy services. Thus, the project includes user studies to assess each service's behavioral impact on users.Broader impacts stem from the project's industrial partnerships with a local utility, a premier research lab (Microsoft Research), a small startup company (Budderfly), and an environmentally-conscious non-profit (WattTime.org). These partnerships will expose students to industry perspectives on addressing real-world energy-efficiency problems, and interdisciplinary research that spans technical and behavioral disciplines. The project will enable sustainability-aware curriculum, undergraduate REU projects, and sustainability-focused outreach efforts to local K-12 students through the newly established Massachusetts Green High Performance Computing Center.The lead institution is the University of Massachusetts Amherst. The primary industry partner is Holyoke Gas and Electric (HG&E)) (Holyoke, MA), a municipal utility. Primary academic partners are the University of Maryland-Baltimore County (Baltimore, MD) and Williams College (Williamstown, MA). Broader context partners are Microsoft (Redmond, WA), a large business, WattTime.org (Berkeley, CA), a non-profit, and Budderfly (Shelton, CT), a small business.

这一创新伙伴关系：建设创新能力(PFI：BIC)项目专注于开发一个公用事业驱动的能源服务平台。由于建筑消耗了社会总能源使用量的很大一部分，即使是建筑能效的轻微改善也有可能产生显著的好处。近年来，公用事业公司部署了数千万个智能电表，记录短时间间隔(例如，每隔几分钟)的建筑物能源使用情况。虽然智能电表最初的目的是支持基本的公用事业操作(例如，自动抄表)，但本项目将其作为开发新一类智能能源服务系统的基础。具体地说，该项目重点分析公用事业智能电表和其他联网传感器提供的海量数据，以提高建筑和电网的能效。这项研究是公用事业驱动的，因为公用事业公司有i)访问海量客户能源数据，ii)向客户提供分析结果的方法，以及iii)提高客户能源效率的强大激励(例如，通过减少高峰需求来降低自己的运营成本)。这种方法是可扩展的，因为它利用了现有的建筑智能仪表数据，而不需要安装新的智能设备和控制系统。该项目将发展公用事业驱动的智能能源服务，以提高电网能源效率，鼓励节能，并推广当地的可再生能源，如屋顶太阳能。研究方法包括为智能电表数据开发新的能源分析技术，以新颖的方式结合这些技术来创建提高能源效率的特定应用程序，将这些应用程序打包为最终用户可访问的基于云的Web服务，并评估它们对用户行为和能源效率的影响。特别是，该项目将开发三个特定的智能能源服务系统：i)通过分析智能电表数据来确定最优恒温器时间表的i计划，ii)绿色需求响应(Green-DR)，以适时地调度弹性负荷，以与可再生能源发电保持一致，以及iii)PowerTrip，向用户提供实时能源事件通知和建议的节能行动。该项目将实施每个系统的原型，并使用来自18,000个智能电表的真实数据(匿名)对其进行评估。这项研究遵循人在环模型，在智能能源服务的设计和实施中整合了技术和用户行为研究。因此，该项目包括评估每项服务对用户行为影响的用户研究。Broader的影响来自该项目与当地公用事业公司、一流的研究实验室(Microsoft Research)、一家小型初创公司(Budderly)和一家具有环保意识的非营利组织(WattTime.org)的行业合作伙伴关系。这些合作关系将使学生接触到解决现实世界能源效率问题的行业视角，以及跨越技术和行为学科的跨学科研究。该项目将通过新成立的马萨诸塞州绿色高性能计算中心，向当地K-12学生提供可持续发展意识课程、本科REU项目和专注于可持续发展的外展工作。牵头机构是马萨诸塞大学阿默斯特分校。主要的行业合作伙伴是市政公用事业公司Holyoke Gas and Electric(HG&amp；E)(位于马萨诸塞州霍利奥克)。主要的学术合作伙伴是马里兰大学巴尔的摩分校(马里兰州巴尔的摩)和威廉姆斯学院(马萨诸塞州威廉姆斯敦)。更广泛的背景合作伙伴是大型企业微软(华盛顿州雷蒙德)、非营利性组织WattTime.org(加利福尼亚州伯克利)和小企业BudderFly(康涅狄格州谢尔顿)。

项目成果

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

On the Feasibility, Cost, and Carbon Emissions of Grid Defection

电网缺陷的可行性、成本和碳排放

• DOI: 10.1109/smartgridcomm.2019.8909691
• 发表时间: 2019
• 期刊: and Computing Technologies for Smart Grids (SmartGridComm
• 作者: Bansal, Akansha Singh;Irwin, David
• 通讯作者: Irwin, David