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 July 31, 2019.

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Methods:

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

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File Inventory:

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


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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 
providers. 

- 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

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License:
	Attribution 4.0 International (CC BY 4.0)

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Suggested Citation:

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.

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