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Experiment on Electricity Consumption Prediction using Long Short-Term Memory Architecture on Residential Electrical Consumer

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dc.citedby2
dc.contributor.authorMd Salleh N.S.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorJorgensen B.N.en_US
dc.contributor.authorid54946009300en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid7202434812en_US
dc.date.accessioned2023-05-29T09:06:50Z
dc.date.available2023-05-29T09:06:50Z
dc.date.issued2021
dc.descriptionBenchmarking; Brain; Electric power utilization; Errors; Forecasting; Global warming; Mean square error; Memory architecture; Quality control; Consumption patterns; Electricity demands; Electricity-consumption; Historical dataset; Independent variables; Mean absolute error; Prediction accuracy; Renewable energies; Long short-term memoryen_US
dc.description.abstractRenewable energy is an alternative for carbon-intensive energy sources that reduce global warming emissions. The electricity demand prediction helps to predict the consumption patterns on the demand side. The historical dataset of electricity usage is an essential source required to perform electricity prediction. This paper proposed the addition of independent variables that includes special days or holidays, weekend, seasons, and daylight duration into the basic electricity usage dataset that helps to increase the prediction accuracy. There were two datasets used in this study, basic electricity usage dataset that consists of date, time, and usage features, and extended electricity usage dataset that consists of the basic and independent variables features. Each dataset produced one model, basic model and extended model, respectively, from the training sessions conducted. The basic electricity usage dataset model was used as a benchmark to evaluate the quality of the model with extended features, extended model. Long-Short Term Memory (LSTM) was the selected machine learning architecture due to its ability to solve the regression problem in time series. All models produced were evaluated using two evaluation metrics, mean squared error (MSE) and mean absolute error (MAE). The application of the proposed methodology, LSTM with the proposed extended features had the lowest error rate with an MSE value of 0.1238 and an MAE value of 0.0388. These results showed that adding independent variables into the dataset improved the model generated from the training session. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9493466
dc.identifier.doi10.1109/ICOTEN52080.2021.9493466
dc.identifier.scopus2-s2.0-85112347445
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85112347445&doi=10.1109%2fICOTEN52080.2021.9493466&partnerID=40&md5=df52784f3bfc7c55f70a84c8b2160792
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26103
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021
dc.titleExperiment on Electricity Consumption Prediction using Long Short-Term Memory Architecture on Residential Electrical Consumeren_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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