skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

Abstract —An accurate representation of the voltage- dependent, time-varying energy consumption of end-use electric loads is essential for the operation of many modern distribution automation (DA) schemes. One such scheme, volt-var optimization (VVO), has been widely deployed because of its ability to decrease energy consumption and peak demand. Many modern VVO schemes leverage electric network models and power flow results to inform control decisions, and are sensitive to errors in the end-use electric load models. Load modeling for utility VVO systems is typi- cally performed based on a load allocation algorithm, which uses a sparse set of measurements to estimate demand for each electric customer. End-use load modeling can potentially be improved using additional measurements, such as from advanced metering infrastructure (AMI). This paper presents two independent and novel machine learning algorithms for creating accurate, data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses historic AMI data, k -means clustering, and least-squares optimization to create a predictive load model for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incor- porate additional data and increase model accuracy for each electric customer. The improved accuracy of themore » load models for both algorithms is validated through simulation.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [2];  [2]
  1. BATTELLE (PACIFIC NW LAB)
  2. Duke Energy
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1598801
Report Number(s):
PNNL-SA-144442
Journal ID: ISSN 2572-6862
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 53rd Hawaii International Conference on System Sciences (HICSS-53), January 6-10, 2020, Maui, Hawaii
Country of Publication:
United States
Language:
English

Citation Formats

Thayer, Brandon L., Engel, David W., Chakraborty, Indrasis, Schneider, Kevin P., Ponder, Leslie, and Fox, Kevin. Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data. United States: N. p., 2020. Web. doi:10.24251/HICSS.2020.373.
Thayer, Brandon L., Engel, David W., Chakraborty, Indrasis, Schneider, Kevin P., Ponder, Leslie, & Fox, Kevin. Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data. United States. doi:10.24251/HICSS.2020.373.
Thayer, Brandon L., Engel, David W., Chakraborty, Indrasis, Schneider, Kevin P., Ponder, Leslie, and Fox, Kevin. Sat . "Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data". United States. doi:10.24251/HICSS.2020.373.
@article{osti_1598801,
title = {Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data},
author = {Thayer, Brandon L. and Engel, David W. and Chakraborty, Indrasis and Schneider, Kevin P. and Ponder, Leslie and Fox, Kevin},
abstractNote = {Abstract —An accurate representation of the voltage- dependent, time-varying energy consumption of end-use electric loads is essential for the operation of many modern distribution automation (DA) schemes. One such scheme, volt-var optimization (VVO), has been widely deployed because of its ability to decrease energy consumption and peak demand. Many modern VVO schemes leverage electric network models and power flow results to inform control decisions, and are sensitive to errors in the end-use electric load models. Load modeling for utility VVO systems is typi- cally performed based on a load allocation algorithm, which uses a sparse set of measurements to estimate demand for each electric customer. End-use load modeling can potentially be improved using additional measurements, such as from advanced metering infrastructure (AMI). This paper presents two independent and novel machine learning algorithms for creating accurate, data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses historic AMI data, k -means clustering, and least-squares optimization to create a predictive load model for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incor- porate additional data and increase model accuracy for each electric customer. The improved accuracy of the load models for both algorithms is validated through simulation.},
doi = {10.24251/HICSS.2020.373},
journal = {},
issn = {2572-6862},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: