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| dc.contributor.author | Ledva, Gregory S. | |
| dc.contributor.author | Zhe, Du | |
| dc.contributor.author | Peterson, Sarah | |
| dc.contributor.author | Balzano, Laura | |
| dc.contributor.author | Mathieu, Johanna L. | |
| dc.date.accessioned | 2019-06-19T18:04:27Z | |
| dc.date.available | 2019-06-19T18:04:27Z | |
| dc.date.issued | 2019-06-19 | |
| dc.identifier.uri | https://hdl.handle.net/2027.42/149480 | |
| dc.description | The contents of this deposit include the papers, posters, and talks that resulted from NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements." The project focused on developing control, estimation, and modeling methods for residential demand response and electric distribution networks. Research Overview: The aim of this project was to incorporate online learning with dynamics into inference algorithms within an electric power system, e.g., for residential demand response or real-time inference in distribution networks. The research was conducted at the University of Michigan in Ann Arbor, MI, USA under NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements", between August 1, 2015 and December 31, 2018. | en_US |
| dc.description | Related code is at the DOI in the dc.relation field of the full item record or https://doi.org/10.7302/xtsr-jx10 | |
| dc.description.sponsorship | National Science Foundation (NSF) - ECCS-1508943 | en_US |
| dc.language.iso | en_US | en_US |
| dc.relation | https://doi.org/10.7302/xtsr-jx10 | |
| dc.subject | online learning | en_US |
| dc.subject | energy disaggregation | en_US |
| dc.subject | residential demand response | en_US |
| dc.subject | networked control | en_US |
| dc.subject | Kalman filter | en_US |
| dc.subject | frequency regulation | en_US |
| dc.title | Inferring the behavior of distributed energy resources from incomplete measurements (project outputs) | en_US |
| dc.type | Project | en_US |
| dc.subject.hlbsecondlevel | Electrical Engineering | |
| dc.subject.hlbsecondlevel | Computer Science | |
| dc.subject.hlbtoplevel | Engineering | |
| dc.contributor.affiliationum | Electrical Engineering and Computer Science | en_US |
| dc.contributor.affiliationumcampus | Ann Arbor | en_US |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/53/readme.txt | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/1/2018_UVM.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/2/2018_UIUC.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/3/2018_SLAC.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/4/2018_PESGM.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/5/2018_NREL.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/6/2018_Defense.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/7/2018_CCTA.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/8/2017_Proposal.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/9/2017_nextGenerationOfResearchersInPowerSystems.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/10/2017_ACC.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/11/2016_UCSD.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/13/2016_MIT.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/14/2016_IMA.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/12/2016_SmartGridComm.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/15/2016_ETH.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/16/2015_Toronto.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/17/2015_Allerton.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/18/2015_Allerton_InferringTheBehavior.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/19/2016_ACC_LinearApproach.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/20/2018_CCTA_ExploringConnections.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/21/2018_EMaRG_DisaggregatingLoad.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/22/2018_PESGM_BenchmarkingAggregate.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/23/2018_TPWRS_ManagingCommunication.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/24/2018_TPWRS_RealTimeEnergy.pdf | |
| dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/149480/25/2018_PESGM_poster.pdf | |
| dc.description.filedescription | Description of 2018_UVM.pdf : Talk - Coordinating Distributed Energy Resources Without Breaking the Bank, or the Grid | |
| dc.description.filedescription | Description of 2018_UIUC.pdf : Talk - Real-Time Energy Disaggregation of a Distribution Feeder’s Demand Using Online Learning | |
| dc.description.filedescription | Description of 2018_SLAC.pdf : Talk - Practical Issues in Automatic, Residential Demand Response | |
| dc.description.filedescription | Description of 2018_PESGM.pdf : Talk - Benchmarking of Aggregate Residential Load Models Used for Demand Response | |
| dc.description.filedescription | Description of 2018_NREL.pdf : Talk - Practical Issues in Automatic, Residential Demand Response | |
| dc.description.filedescription | Description of 2018_Defense.pdf : Talk - Learning and Control Applied to Demand Response and Electricity Distribution Networks | |
| dc.description.filedescription | Description of 2018_CCTA.pdf : Talk - Exploring Connections Between a Multiple Model Kalman Filter and Dynamic Fixed Share with Applications to Demand Response | |
| dc.description.filedescription | Description of 2017_Proposal.pdf : Talk - Practical Issues in Automatic, Residential Demand Response | |
| dc.description.filedescription | Description of 2017_nextGenerationOfResearchersInPowerSystems.pdf : Talk - 9th Seminar for the Next Generation of Researchers in Power Systems | |
| dc.description.filedescription | Description of 2017_ACC.pdf : Talk - A Linear Approach to Manage Input Delays While Supplying Frequency Regulation Using Residential Loads | |
| dc.description.filedescription | Description of 2016_UCSD.pdf : Talk - Inference and control of electric loads given sparse measurements and communications delays | |
| dc.description.filedescription | Description of 2016_MIT.pdf : Talk - Inference and control of electric loads given sparse measurements and communications delays | |
| dc.description.filedescription | Description of 2016_IMA.pdf : Talk - Inferring the behavior of distributed flexible electric loads | |
| dc.description.filedescription | Description of 2016_SmartGridComm.pdf : Talk - Engaging Distributed Flexible Electric Loads in Power System Opera<on | |
| dc.description.filedescription | Description of 2016_ETH.pdf : Talk - Managing Communication Delays and Model Error in Demand Response | |
| dc.description.filedescription | Description of 2015_Toronto.pdf : Talk - Inference and control of distributed energy resources with sparse measurements and communications delays | |
| dc.description.filedescription | Description of 2015_Allerton.pdf : Talk - Inferring the Behavior of Distributed Energy Resources with Online Learning | |
| dc.description.filedescription | Description of 2015_Allerton_InferringTheBehavior.pdf : Paper - Inferring the Behavior of Distributed Energy Resources with Online Learning | |
| dc.description.filedescription | Description of 2016_ACC_LinearApproach.pdf : Paper - A Linear Approach to Manage Input Delays While Supplying Frequency Regulation Using Residential Loads | |
| dc.description.filedescription | Description of 2018_CCTA_ExploringConnections.pdf : Paper - Exploring Connections Between a Multiple Model Kalman Filter and Dynamic Fixed Share with Applications to Demand Response | |
| dc.description.filedescription | Description of 2018_EMaRG_DisaggregatingLoad.pdf : Paper - Disaggregating Load by Type from Distribution System Measurements in Real-Time | |
| dc.description.filedescription | Description of 2018_PESGM_BenchmarkingAggregate.pdf : Paper - Benchmarking of Aggregate Residential Load Models Used for Demand Response | |
| dc.description.filedescription | Description of 2018_TPWRS_ManagingCommunication.pdf : Paper - Managing Communication Delays and Model Error in Demand Response for Frequency Regulation | |
| dc.description.filedescription | Description of 2018_TPWRS_RealTimeEnergy.pdf : Paper - Real-Time Energy Disaggregation of a Distribution Feeder’s Demand Using Online Learning | |
| dc.description.filedescription | Description of 2018_PESGM_poster.pdf : Poster - Benchmarking of Aggregate Residential Load Models Used for Demand Response | |
| dc.description.filedescription | Description of readme.txt : Project Readme file | |
| dc.description.filedescription | Description of readme.txt : Project Readme file | |
| dcterms.abstract | Research Overview: The aim of this project was to incorporate online learning with dynamics into inference algorithms within an electric power system, e.g., for residential demand response or real-time inference in distribution networks. The research was conducted at the University of Michigan in Ann Arbor, MI, USA under NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements", between August 1, 2015 and December 31, 2018. | |
| dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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