Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances

TL;DR

This paper introduces three hierarchical clustering methods using quantile autocovariances and autocorrelations to efficiently group household electricity load patterns from smart meters, aiding in understanding consumption behaviors.

Contribution

It proposes novel clustering techniques based on quantile autocovariances that are scalable, robust, and computationally efficient for large smart meter datasets.

Findings

Clusters reveal meaningful household consumption behaviors.

Methods effectively capture geo-demographic segmentation.

Features identified as key indicators for cluster differentiation.

Abstract

In order to improve the efficiency and sustainability of electricity systems, most countries worldwide are deploying advanced metering infrastructures, and in particular household smart meters, in the residential sector. This technology is able to record electricity load time series at a very high frequency rates, information that can be exploited to develop new clustering models to group individual households by similar consumptions patterns. To this end, in this work we propose three hierarchical clustering methodologies that allow capturing different characteristics of the time series. These are based on a set of "dissimilarity" measures computed over different features: quantile auto-covariances, and simple and partial autocorrelations. The main advantage is that they allow summarizing each time series in a few representative features so that they are computationally efficient, robust against outliers, easy to automatize, and scalable to hundreds of thousands of smart meters series. We evaluate the performance of each clustering model in a real-world smart meter dataset with thousands of half-hourly time series. The results show how the obtained clusters identify relevant consumption behaviors of households and capture part of their geo-demographic segmentation. Moreover, we apply a supervised classification procedure to explore which features are more relevant to define each cluster.