Publication:
Energy Usage Prediction for Smart Home with Regression Based Ensemble Model

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dc.citedby3
dc.contributor.authorHoque M.S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorSaharudin S.A.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorid57220806665en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57216296367en_US
dc.contributor.authorid7102424614en_US
dc.date.accessioned2023-05-29T08:08:03Z
dc.date.available2023-05-29T08:08:03Z
dc.date.issued2020
dc.descriptionAir 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 analyticsen_US
dc.description.abstractResidential 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243578
dc.identifier.doi10.1109/ICIMU49871.2020.9243578
dc.identifier.epage383
dc.identifier.scopus2-s2.0-85097650660
dc.identifier.spage378
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097650660&doi=10.1109%2fICIMU49871.2020.9243578&partnerID=40&md5=2039ed8524aab5005ba5cd2897d6654a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25311
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleEnergy Usage Prediction for Smart Home with Regression Based Ensemble Modelen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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