A Clustering Framework for Residential Electric Demand Profiles

TL;DR

This paper presents a clustering framework for residential electricity demand profiles using dimensionality reduction and unsupervised clustering, validated through a novel objective strategy and subjective verification, to classify household consumption patterns.

Contribution

It introduces a novel validation strategy for selecting optimal algorithm combinations in clustering demand profiles, enhancing classification accuracy.

Findings

Effective clustering of household demand profiles achieved.

Validation strategy improves algorithm selection process.

Framework applicable to large-scale smart meter data.

Abstract

The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.