Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data
Files
Date
2020-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
An 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.
Description
Keywords
Monitoring, Control, and Protection, load flow, load modeling, machine learning, power distribution, power system modeling
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 53rd Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.