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

Conference · Tue Jan 05 04:00:00 EST 2021

DOI:https://doi.org/10.1109/TD39804.2020.9299915· OSTI ID:1764947

Ausmus, Jason R.; Sen, Pankaj K.; Wu, Tianying; Adhikari, Uttam; Zhang, Yingchen; Krishnan, Venkat

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1764947
• Report Number(s): NREL/CP-5D00-79050; MainId:32967; UUID:d2d35f04-379d-4382-86ef-edfbd7771eec; MainAdminID:19299
• Country of Publication: United States
• Language: English

Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
clustering
electric load profiles
machine learning
pattern recognition
time-series