Two-stage building energy consumption clustering based on temporal and peak demand patterns

TL;DR

This paper introduces a two-stage clustering approach for building energy consumption data that improves the accuracy of segmenting load patterns and peak demands, aiding better energy management.

Contribution

The novel two-stage clustering method captures load shape variations and peak demands more accurately than existing techniques, enhancing energy consumption segmentation.

Findings

Improved clustering accuracy over baseline methods.

Effective segmentation of load shapes and peak demands.

Demonstrated on three datasets with ~15,000 profiles.

Abstract

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.