Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Jason Ausmus, Pankaj Sen, Tianying Wu, Uttam Adhikari, Yingchen Zhang, Venkat Krishnan

Research output: Contribution to conferencePaper

3 Scopus Citations

Abstract

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2021
Event2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) - Chicago, Illinois
Duration: 12 Oct 202015 Oct 2020

Conference

Conference2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)
CityChicago, Illinois
Period12/10/2015/10/20

NREL Publication Number

  • NREL/CP-5D00-79050

Keywords

  • clustering
  • electric load profiles
  • machine learning
  • pattern recognition
  • time-series

Fingerprint

Dive into the research topics of 'Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping'. Together they form a unique fingerprint.

Cite this