课题基金 / 基金详情

PFI:BIC - Utility-driven Smart Energy Services

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:
$100.0万
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
项目状态:
已结题
起止时间:
2015-09-01 至 2019-08-31

项目摘要

项目成果

Prashant Shenoy的其他基金

相似基金

相关文献

中文摘要
翻译
创新伙伴关系:建设创新能力(PFI:BIC)项目侧重于开发公用事业驱动的能源服务平台。由于建筑物消耗了社会总能源使用量的很大一部分,因此即使建筑物能源效率的适度改进也有可能产生显著的效益。近年来,公用事业公司已经部署了数以千万计的智能电表,这些电表可以在短时间间隔内(例如每隔几分钟)记录建筑物的能源使用情况。虽然智能电表的最初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型智能能源服务系统的基础。具体来说,该项目侧重于分析来自公用事业智能电表和其他联网传感器的大量数据,以提高建筑物和电网的能源效率。这项研究是由公用事业公司驱动的,因为公用事业公司可以访问大量的客户能源数据,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烈动机(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电表数据,而不需要安装新的智能设备和控制系统。该项目将开发公用事业驱动的智能能源服务,以提高电网能效,鼓励节约能源,并促进当地可再生能源的使用,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网络服务,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) iProgram通过分析智能电表数据来确定最佳恒温器时间表,ii)绿色需求响应(Green- dr)来根据可再生能源发电机会地安排弹性负荷,以及iii) PowerTrip为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,并根据来自18,000个智能电表的真实数据(匿名)对其进行评估。该研究遵循人在循环模型,在智能能源服务的设计和实施中集成了技术和用户行为研究。因此,该项目包括用户研究,以评估每种服务对用户的行为影响。更广泛的影响来自于项目与当地公用事业公司、一流研究实验室(微软研究院)、小型初创公司(Budderfly)和环保非营利组织(WattTime.org)的工业伙伴关系。这些合作伙伴关系将使学生接触到解决现实世界能源效率问题的行业观点,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,为当地K-12学生提供可持续发展意识课程、本科REU项目和以可持续发展为重点的推广工作。领头的机构是马萨诸塞大学阿姆赫斯特分校。主要的行业合作伙伴是霍利奥克燃气和电力公司(HG&E)(马萨诸塞州霍利奥克),一家市政公用事业公司。主要的学术合作伙伴是马里兰大学-巴尔的摩县(马里兰州巴尔的摩)和威廉姆斯学院(马萨诸塞州威廉斯敦)。更广泛的合作伙伴包括大型企业Microsoft(华盛顿州雷德蒙德)、非营利组织WattTime.org(加利福尼亚州伯克利)和小型企业Budderfly(华盛顿州谢尔顿)。
英文摘要
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.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
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
影响因子: --
作者: [Lee, Stephen, Shenoy, Prashant, Ramamritham, Krithi, Irwin, David]
通讯作者: Irwin, David
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
影响因子: --
作者: [Stephen Lee;Srinivasan Iyengar;Menghong Feng;Prashant J. Shenoy;Subhransu Maji]
通讯作者: 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
影响因子: --
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
影响因子: --
作者: [Chen, Dong, Irwin, David]
通讯作者: Irwin, David
Collaborative Research: CNS Core: Medium: IoCT: System Mechanisms for Enabling an Internet of Collaborative Things
  • 批准号:
    2211302
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.87万
  • 财政年份:
    2022
  • 负责人:
    Prashant Shenoy
  • 依托单位:
CNS Core: Medium:Model-driven Resource Management for Avoiding Performance Pitfalls in Edge Computing
  • 批准号:
    2211888
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $119.91万
  • 财政年份:
    2022
  • 负责人:
    Prashant Shenoy
  • 依托单位:
Collaborative Research: NGSDI: CarbonFirst: A Sustainable and Reliable Carbon-Centric Cloud-Edge Software Infrastructure
  • 批准号:
    2105494
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $108.57万
  • 财政年份:
    2021
  • 负责人:
    Prashant Shenoy
  • 依托单位:
ICE-T: RC: Horizontal Resource Management in Distributed Edge Clouds
  • 批准号:
    1836752
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2018
  • 负责人:
    Prashant Shenoy
  • 依托单位:
国内基金
海外基金
金属-介电杂化BIC的多模式耦合与调控研究
  • 批准号:
    2026JJ90077
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
    蒋藩
  • 依托单位:
高效率、 多功能太赫兹非局域BIC超表面波前调制器
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2025
  • 负责人:
    凡俊兴
  • 依托单位:
BIC/FTC/TAF治疗HIV感染者身体成份与代谢指标的变化趋势及影响因素的研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2025
  • 负责人:
    闫俊
  • 依托单位:
高性能单向面发射拓扑BIC光子晶体激光器的研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    15.0万元
  • 批准年份:
    2024
  • 负责人:
    曾永全
  • 依托单位: