Detecting Air Conditioning Usage in Households Using Unsupervised Machine Learning on Smart Meter Data

Abstract

This article presents an unsupervised machine learning approach for the problem of detecting use of air conditioning in households, during the summer. This is a relevant problem in the context of the modern smart grid approach under the paradigm of smart cities. The proposed methodology applies data analysis, a thermal inertial model for estimating the temperature inside a household, statistical analysis, clustering, and classification. The proposed model is validated on a real case study, considering households with known use of air conditioning in summer. In the evaluation, the proposed classification methodology reached an accuracy of 0.897, a promising result considering the very small cardinality of the set of households. The proposed method is valuable since it applies an unsupervised approach, which does not require large volumes of labeled data for training, and allows determining characteristics in the electricity consumption patterns that are useful for categorization. In turn, it is a non-intrusive method and does not require investing in the installation of complex devices or conducting consumer surveys.