Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home

This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. T...

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Main Authors: Husin N.S.I.M., Mostafa S.A., Jaber M.M., Gunasekaran S.S., Al-Shakarchi A.H., Abdulsattar N.F.
Other Authors: 58581629000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
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spelling my.uniten.dspace-344662024-10-14T11:19:58Z Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home Husin N.S.I.M. Mostafa S.A. Jaber M.M. Gunasekaran S.S. Al-Shakarchi A.H. Abdulsattar N.F. 58581629000 37036085800 56519461300 55652730500 57218596226 57866675600 Appliances Energy Estimation Energy Management Machine Learning Time Series Dataset Automation Data mining Decision trees Energy conservation Energy management Forecasting Learning algorithms Learning systems Machine learning Regression analysis Time series Appliance energy estimation Energy estimation Energy-consumption Machine learning algorithms Machine-learning Smart homes Three models Time series dataset Times series Training and testing Energy utilization This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models' performance in predicting the appliances' energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively. � 2023 IEEE. Final 2024-10-14T03:19:58Z 2024-10-14T03:19:58Z 2023 Conference Paper 10.1109/AICCIT57614.2023.10217991 2-s2.0-85171348169 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171348169&doi=10.1109%2fAICCIT57614.2023.10217991&partnerID=40&md5=0d6a861a50a6edc60fc87b0ec36b9418 https://irepository.uniten.edu.my/handle/123456789/34466 229 233 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Appliances Energy Estimation
Energy Management
Machine Learning
Time Series Dataset
Automation
Data mining
Decision trees
Energy conservation
Energy management
Forecasting
Learning algorithms
Learning systems
Machine learning
Regression analysis
Time series
Appliance energy estimation
Energy estimation
Energy-consumption
Machine learning algorithms
Machine-learning
Smart homes
Three models
Time series dataset
Times series
Training and testing
Energy utilization
spellingShingle Appliances Energy Estimation
Energy Management
Machine Learning
Time Series Dataset
Automation
Data mining
Decision trees
Energy conservation
Energy management
Forecasting
Learning algorithms
Learning systems
Machine learning
Regression analysis
Time series
Appliance energy estimation
Energy estimation
Energy-consumption
Machine learning algorithms
Machine-learning
Smart homes
Three models
Time series dataset
Times series
Training and testing
Energy utilization
Husin N.S.I.M.
Mostafa S.A.
Jaber M.M.
Gunasekaran S.S.
Al-Shakarchi A.H.
Abdulsattar N.F.
Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
description This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models' performance in predicting the appliances' energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively. � 2023 IEEE.
author2 58581629000
author_facet 58581629000
Husin N.S.I.M.
Mostafa S.A.
Jaber M.M.
Gunasekaran S.S.
Al-Shakarchi A.H.
Abdulsattar N.F.
format Conference Paper
author Husin N.S.I.M.
Mostafa S.A.
Jaber M.M.
Gunasekaran S.S.
Al-Shakarchi A.H.
Abdulsattar N.F.
author_sort Husin N.S.I.M.
title Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
title_short Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
title_full Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
title_fullStr Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
title_full_unstemmed Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home
title_sort machine learning regression approach for estimating energy consumption of appliances in smart home
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061058109734912
score 13.214268