Two-Stage Clustering of Household Electricity Load Shapes based on Temporal Pattern Peak Demand

08/10/2020 ∙ by Milad Afzalan, et al. ∙ 0

Analyzing smart meter data to understand energy consumption patterns helps utilities and energy providers perform customized demand response operations. Existing energy consumption segmentation techniques use assumptions that could result in reduced quality of clusters in representing their members. We address this limitation by introducing a two-stage clustering method that more accurately captures load shape temporal patterns and peak demands. In the first stage, load shapes are clustered by allowing a large number of clusters to accurately capture variations in energy use patterns and cluster centroids are extracted by accounting for shape misalignments. In the second stage, clusters of similar centroid and power magnitude range are merged by using Dynamic Time Warping. We used three datasets consisting of  250 households ( 15000 profiles) to demonstrate the performance improvement, compared to baseline methods, and discuss the impact on energy management.



There are no comments yet.


This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.