| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | July 30, 2011 |
| Latest Amendment Date: | November 22, 2016 |
| Award Number: | 1115798 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2011 |
| End Date: | July 31, 2017 (Estimated) |
| Total Intended Award Amount: | $179,863.00 |
| Total Awarded Amount to Date: | $179,863.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2130 FULTON ST SAN FRANCISCO CA US 94117 (415)422-5203 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2130 FULTON ST SAN FRANCISCO CA US 94117 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CSR-Computer Systems Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Encouraging broader adoption of renewable energy sources is key to minimizing our dependence on the electric grid. Though installation of solar panels at homes is increasingly common, the process of managing the energy they generate is both manual and ad hoc. In this work, the PIs are building an infrastructure for data collection and analysis of energy generation, energy consumption, and user behavior in green homes; and an an integrated approach to green home energy management validated using a novel recommendation-based evaluation platform. Using a custom-built measurement infrastructure, the project conducts a broad study of homes powered by a variety of renewable sources. The study examines both energy generation by renewable sources as well as how and why energy is consumed by a variety of devices. The results inform the design of a holistic control system that matches predicted supply with demand of a distributed set of devices in the home. Moreover, the system is being deployed in a few homes and evaluated using a recommendation system implemented as a mobile application that suggests when users should run devices.
This project supports research critical to encouraging adoption of more environmentally responsible practices in the home and enables a collaboration between PI Banerjee, who teaches at an EPSCoR institution, and PI Rollins, who teaches at an undergraduate institution. Further, the project is developing a CS1 course that will increase awareness of green energy concepts by introducing computing through collection and analysis of data on energy consumption practices.
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PROJECT OUTCOMES REPORT
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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
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The Green Homes project has developed technology and techniques to both understand
home energy requirements as well as help users make better energy decisions, for example
by reducing energy consumption or intelligently leveraging renewable sources such as solar. This project was one of the first to consider energy management in completely off-grid homes powered by solar. An initial study of a completely off-grid solar home demonstrated that traditional energy management techniques, for example running high-power appliances like dishwashers between 7PM and 7AM, are inappropriate for homes powered by renewable technologies. Over a three-year period, the project collected data on energy usage of individual appliances in nine different homes, most grid-connected. Analysis of this dataset demonstrated that appliance use often happens at the same time of day and some pairs of appliances, for example a lamp and TV in the same room, are often used together. Analysis also demonstrated associations between appliance use and activities, for example chores or cooking, performed by the user. Despite these associations, the project discovered large variation in how well state-of-the-art algorithms are able to predict energy requirements in homes, demonstrating that personalized approaches for home energy management are likely to be most successful. The final component of the project demonstrated the feasibility of incorporating personalization into a home energy management system by using visualizations of past energy usage as well as using an in situ notification system to recommend appropriate lighting levels in the home.
This project addresses an area of critical importance---making energy consumption more sustainable---and has also offered training opportunities to a variety of students including many undergraduates. The project offers an understanding of social and behavioral issues governing adoption of sustainability practices in the home, laying the foundation for new mechanisms that will reducing our dependence on the electric grid and fossil fuels. Further, the project has involved a significant number of both undergraduate and Master’s level in addition to the PhD students who have contributed. One of the undergraduates has continued to a PhD program and many of them have secured jobs in the technology industry that are directly related to their contributions to the project.
Last Modified: 08/31/2017
Modified by: Sami Rollins
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