Computational intelligence for residential electricity consumption assessment: Detecting air conditioner use in households

Abstract

This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (k-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers for a case study in Uruguay, using data collected by a smart meters network and open weather data. Data from 29 Uruguayan cities were considered in the period from January 1, 2022, to December 31, 2022. Two thermal models are developed for estimating the temperature inside households. The main results indicate that the proposed approach is able to reach a high classification accuracy, up to 94.5% and a high classification recall, up to 95%, for the considered real case study. The final scope of the work is developing smart tools for classifying consumers, to design and suggest specific commercial products that promote energy efficiency.