Detecting abnormal electricity usage using unsupervised learning model in unlabeled data

Jesmeen Et Al

doi:10.21833/ijaas.2021.09.014

Abstract

Smart-home systems achieved great popularity in the last decade as they increase the comfort and quality of life. Reduction of energy consumption became a very important desiderate in the context of the explosive technological development of modern society with a major impact on the future development of mankind. Moreover, due to the large amount of data available from smart meters installed in households. It makes leverage to able to find data abnormalities for better monitoring and forecasting. Detecting data anomalies helps in making a better decision for reducing energy usage wasted. In recent years, machine learning models are widely used for developing intelligent systems. Currently, researchers’ main focus is on developing supervised learning models for predicting anomalies. However, there are challenges to train models with unlabeled data indicating data anomaly or not. In this paper, abnormalities are detected in electricity usage using unsupervised learning and evaluated using Excess Mass. The unsupervised anomaly detection model is based on Gaussian Mixture Model (GMM) and Isolation Forest (iForest). The models are compared with Local Outlier Factor (LOF) and One-class support vector machine (OCSVM). The proposed framework is tested with actual electricity usage and temperature data obtained from Numenta Anomaly Benchmark (NAB), which contains normal and anomaly data in time series. Finally, it has been observed that the iForest out-performed as the detection model for the selected use case. The outcome showed that the iForest can quickly detect anomalies in electricity usage data with only a sequence of data without feature extraction. The proposed model is suitable for the Smart Home Energy Management System's practical requirement and can be implemented in various houses independently. The proposed system can also be extended with the various use cases having similar data types.

Highlights

Smart-home systems achieved great popularity in the last decade as they increase the comfort and quality of life
As MEC (2014) said, it does not require significant physical changes to have electricity and contribute to a greener environment. It was reported in Malaysia and in different places in the world; during the Movement Control Order (MCO) and Conditional Movement Control Order (CMCO) period in COVID-19, electricity consumption in the household increased
Like energy consumption data, Isolation Forest (iForest) and Gaussian Mixture Model (GMM) had out-perform the detection of anomaly process

Summary

Introduction

Smart-home systems achieved great popularity in the last decade as they increase the comfort and quality of life. Detecting data anomalies helps in making a better decision for reducing energy usage wasted. Abnormalities are detected in electricity usage using unsupervised learning and evaluated using Excess Mass. The unsupervised anomaly detection model is based on Gaussian Mixture Model (GMM) and Isolation Forest (iForest). The outcome showed that the iForest can quickly detect anomalies in electricity usage data with only a sequence of data without feature extraction. As MEC (2014) said, it does not require significant physical changes to have electricity and contribute to a greener environment It was reported in Malaysia and in different places in the world; during the Movement Control Order (MCO) and Conditional Movement Control Order (CMCO) period in COVID-19, electricity consumption in the household increased. The task is to train the unsupervised machine learning (ML) model, i.e., Gaussian Mixture Model (GMM) and Isolation Forest (iForest), for anomaly detection in sequence batch Active power data.

Methods

Results

Discussion

Conclusion