Title: Inferring the behavior of distributed energy resources from incomplete measurements (project outputs) Open Access Deposited
|ORSP grant number|
|Citations to related material|
|Related items in Deep Blue|
(2019). Inferring the behavior of distributed energy resources from incomplete measurements (project outputs) [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/xtsr-jx10
Files (Count: 2; Size: 2.51 GB)
|Thumbnail||Title||Original Upload||Last Modified||File Size||Access||Actions|
|readme.txt||2019-06-12||2019-06-28||2.32 KB||Open Access||
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 July 31, 2019.
The methods used in this work fall into the following categories:
1) Aggregate modeling of demand-responsive loads
2) Optimal control using MPC and LQR
3) Estimation using Kalman filtering and online learning
Files in Deep Blue Data (https://doi.org/10.7302/xtsr-jx10):
- "2014_EPCN_code.zip" contains the code generated from this grant and an additional readme to explain its contents further
Files in Deep Blue ( http://hdl.handle.net/2027.42/149480)
- "paper" contains pdfs of the papers generated from this grant
- "poster" contains a pdf of a posted generated from this grant
- "talk" contains pdfs of any talks given based on the work from this grant
Use and Access:
-All code was developed using MATLAB 2014a.
-Raw data from the Pecan Street Dataport ( https://www.pecanstreet.org/dataport/ ) and pertaining to commercial
buildings in CA were excluded from the code as per the license or agreement with the data
- Please cite the relevant paper and this dataset (see below for citation) if the code is used for future work.
Relevant papers are listed in the "code" readme for each directory of code.
- Additional details about the code can be found in the readme file within the "code" directory
Attribution 4.0 International (CC BY 4.0)
G.S. Ledva, L. Balzano, and J.L. Mathieu,
Inferring the behavior of distributed energy resources from incomplete measurements (project outputs),
Mlibrary Deep Blue Data, 2019.