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

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 wi...

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Autores principales: Kasaraneni, Purna Prakash, Venkata Pavan Kumar, Yellapragada, Moganti, Ganesh Lakshmana Kumar, Kannan, Ramani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741071/
https://www.ncbi.nlm.nih.gov/pubmed/36502025
http://dx.doi.org/10.3390/s22239323
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author Kasaraneni, Purna Prakash
Venkata Pavan Kumar, Yellapragada
Moganti, Ganesh Lakshmana Kumar
Kannan, Ramani
author_facet Kasaraneni, Purna Prakash
Venkata Pavan Kumar, Yellapragada
Moganti, Ganesh Lakshmana Kumar
Kannan, Ramani
author_sort Kasaraneni, Purna Prakash
collection PubMed
description 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.
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spelling pubmed-97410712022-12-11 Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data Kasaraneni, Purna Prakash Venkata Pavan Kumar, Yellapragada Moganti, Ganesh Lakshmana Kumar Kannan, Ramani Sensors (Basel) Article 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. MDPI 2022-11-30 /pmc/articles/PMC9741071/ /pubmed/36502025 http://dx.doi.org/10.3390/s22239323 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kasaraneni, Purna Prakash
Venkata Pavan Kumar, Yellapragada
Moganti, Ganesh Lakshmana Kumar
Kannan, Ramani
Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title_full Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title_fullStr Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title_full_unstemmed Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title_short Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
title_sort machine learning-based ensemble classifiers for anomaly handling in smart home energy consumption data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741071/
https://www.ncbi.nlm.nih.gov/pubmed/36502025
http://dx.doi.org/10.3390/s22239323
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