Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data

Simple item page
dc.contributor.authorThayer, Brandon
dc.contributor.authorEngel, Dave
dc.contributor.authorChakraborty, Indrasis
dc.contributor.authorSchneider, Kevin
dc.contributor.authorPonder, Leslie
dc.contributor.authorFox, Kevin
dc.date.accessioned2020-01-04T07:47:12Z
dc.date.available2020-01-04T07:47:12Z
dc.date.issued2020-01-07
dc.description.abstractAn accurate representation of the voltage-dependent, time-varying energy consumption of end-use electric loads is essential for the operation of modern distribution automation (DA) schemes. Volt-var optimization (VVO), a DA scheme which can decrease energy consumption and peak demand, often leverages electric network models and power flow results to inform control decisions, making it sensitive to errors in load models. End-use load modeling can be improved with additional measurements from advanced metering infrastructure (AMI). This paper presents two novel machine learning algorithms for creating data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses AMI data, k-means clustering, and least-squares optimization to create predictive load models for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incorporate additional data and increase model accuracy. The improved accuracy of the load models for both algorithms is validated through simulation.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.373
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64115
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMonitoring, Control, and Protection
dc.subjectload flow
dc.subjectload modeling
dc.subjectmachine learning
dc.subjectpower distribution
dc.subjectpower system modeling
dc.titleImproving End-Use Load Modeling Using Machine Learning and Smart Meter Data
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0301.pdf
Size:
3.98 MB
Format:
Adobe Portable Document Format
Download

Collections

Monitoring, Control, and Protection