Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Abstract

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.

Comprehensive Bibliometric Analysis on Smart Grids: Key Concepts and Research Trends

“…A systematic analysis was conducted to enumerate the duplicate records in the energy consumption data of smart buildings [4] A systematic analysis was conducted to identify the behavior of redundancy in smart home power consumption [5] A systematic three-step method was conducted to learn the abnormal records in smart home power consumption [6] Machine learning-based techniques were implemented to handle various data anomalies in smart home power consumption [7] The missing-reading information in smart home power consumption was detected by implementing an effective and easy approach [8]…”

Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes

“…The electricity consumption of smart buildings/homes plays a vital role in energy management and this energy management should be further enhanced (Aguilar et al, 2021). This energy management helps to improve the smart grid's functionality in various aspects such as demand-side management, thwarting blackouts, and high-quality power to the consumers (Purna Prakash et al, 2022a; 2022b). But to achieve these functionalities, the energy consumption data should not contain any anomalies (Purna Prakash and Pavan Kumar, 2022c).…”

“…But to achieve these functionalities, the energy consumption data should not contain any anomalies (Purna Prakash and Pavan Kumar, 2022c). Some of the most commonly occurring anomalies in energy consumption are missing data, redundant data, and outliers (Purna Prakash and Pavan Kumar, 2021, Purna Prakash and Pavan Kumar, 2022d). Energy data digitalization has created new opportunities to find out such anomalies easily (Leiria et al, 2021).…”

“…An alternate learning and multivariate imputation by chained equations strategies are used to impute the missing data further to maintain data integrity (Lai et al, 2020, Wu et al, 2022). Machine learning techniques are helpful in handling data anomalies in smart home power consumption (Purna Prakash and Pavan Kumar, 2022d). Along with these techniques, a complete survey is executed to understand the applicability of missing data imputation techniques (Miao et al, 2023).…”

Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters

“…An algorithm based on a CNN and GRU was proposed, and the data were tested in real time on measurements from the State Grid Corporation of China. The authors of [5] addressed the analysis of measurements with smart meters for households but in the context of anomalies in the recorded data rather than instantaneous power consumption. Random forest, support vector machine, decision tree, naive Bayes, K-nearest neighbor, and neural network algorithms were used to detect anomalies.…”