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Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory

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dc.citedby2
dc.contributor.authorSalleh N.S.M.en_US
dc.contributor.authorSaripuddin M.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorJorgensen B.N.en_US
dc.contributor.authorid54946009300en_US
dc.contributor.authorid57220806580en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid7202434812en_US
dc.date.accessioned2023-05-29T09:05:58Z
dc.date.available2023-05-29T09:05:58Z
dc.date.issued2021
dc.descriptionAnomaly detection; Brain; Errors; Forecasting; Gradient methods; Mean square error; Optimization; Statistics; Stochastic models; Stochastic systems; Anomaly detection; Electricity load; Electricity theft; Load predictions; Mean absolute error; Mean squared error; Optimizers; Point detection; Power providers; Unsupervised techniques; Long short-term memoryen_US
dc.description.abstractElectricity theft caused a major loss for electricity power provider. The anomaly detection helps to predict the abnormal load usage of a consumer. Usually, the classification method used in anomaly detection. This research paper proposed to identify the potential anomaly points by using threshold and outliers. The prediction in time-series applied Long Short-Term Memory (LSTM) algorithm. The historical electricity load dataset of a single industrial consumer was used to generate the prediction of electricity load. There were five optimizers used to produce the model: Adam, Adadelta, Adagrad, RMSProp, and Stochastic gradient descent (SGD). The prediction model was evaluated using mean squared error (MSE) and mean absolute error (MAE). The best model among all five models was generated by Adadelta optimizer with the error rate value of 0.091982 for MSE and 0.018433 for MAE. The prediction values were generated by this model. The anomaly point was detected by using threshold and outliers. The threshold value was 0.218983. One week in August 2019 was chosen to detect any anomaly load occurrences. There were 24 outliers were found within the selected week. The study shall expand on the electricity usage trend during COVID-19 pandemic period. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/AiDAS53897.2021.9574184
dc.identifier.scopus2-s2.0-85118992042
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118992042&doi=10.1109%2fAiDAS53897.2021.9574184&partnerID=40&md5=e5387eec6d85d02a12bf289409bb5fdb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25997
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 2nd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2021
dc.titleElectricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memoryen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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