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Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

Air conditioning; Ambient intelligence; Automation; Electric utilities; Energy utilization; Forecasting; Intelligent buildings; Mean square error; Automated optimization; Electricity distribution; Ensemble modeling; Ensemble prediction; Normalized absolute errors; Prediction model; Residential secto...

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Main Authors: Hoque M.S., Jamil N., Saharudin S.A., Amin N.
Other Authors: 57220806665
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-253112023-05-29T16:08:03Z Energy Usage Prediction for Smart Home with Regression Based Ensemble Model Hoque M.S. Jamil N. Saharudin S.A. Amin N. 57220806665 36682671900 57216296367 7102424614 Air conditioning; Ambient intelligence; Automation; Electric utilities; Energy utilization; Forecasting; Intelligent buildings; Mean square error; Automated optimization; Electricity distribution; Ensemble modeling; Ensemble prediction; Normalized absolute errors; Prediction model; Residential sectors; Root mean squared errors; Predictive analytics Residential sectors using energy mainly though lighting and HV AC (Heating, Ventilation and Air-Conditioning) have become a significant consumer of world energy and it is expected to grow especially with the trend of increasing smart homes. To provide an optimum, accurate and reliable electricity distribution, load prediction is a prerequisite policy and operational implementation. Smart homes with the use of various sensors create big data that gives a favorable opportunity for developing data-driven energy usage prediction models. In this paper, a novel regression-based ensemble prediction model with inbuilt automated optimization for parameters is proposed to predict the demand of electricity. The model explains the 0.998 correlation between the features and their label, and achieved root mean squared error (RMSE) and Normalized Absolute Error as low as 5.508 and 0.0508 respectively. We have also proposed a novel data-driven classification of the energy usage by unsupervised learning through clustering. � 2020 IEEE. Final 2023-05-29T08:08:03Z 2023-05-29T08:08:03Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243578 2-s2.0-85097650660 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097650660&doi=10.1109%2fICIMU49871.2020.9243578&partnerID=40&md5=2039ed8524aab5005ba5cd2897d6654a https://irepository.uniten.edu.my/handle/123456789/25311 9243578 378 383 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/
description Air conditioning; Ambient intelligence; Automation; Electric utilities; Energy utilization; Forecasting; Intelligent buildings; Mean square error; Automated optimization; Electricity distribution; Ensemble modeling; Ensemble prediction; Normalized absolute errors; Prediction model; Residential sectors; Root mean squared errors; Predictive analytics
author2 57220806665
author_facet 57220806665
Hoque M.S.
Jamil N.
Saharudin S.A.
Amin N.
format Conference Paper
author Hoque M.S.
Jamil N.
Saharudin S.A.
Amin N.
spellingShingle Hoque M.S.
Jamil N.
Saharudin S.A.
Amin N.
Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
author_sort Hoque M.S.
title Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
title_short Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
title_full Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
title_fullStr Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
title_full_unstemmed Energy Usage Prediction for Smart Home with Regression Based Ensemble Model
title_sort energy usage prediction for smart home with regression based ensemble model
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426176159219712
score 13.211869

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