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

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

Anomaly 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 p...

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Main Authors:	Salleh N.S.M., Saripuddin M., Suliman A., Jorgensen B.N.
Other Authors:	54946009300
Format:	Conference Paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
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id	my.uniten.dspace-25997
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spelling	my.uniten.dspace-259972023-05-29T17:05:58Z Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory Salleh N.S.M. Saripuddin M. Suliman A. Jorgensen B.N. 54946009300 57220806580 25825739000 7202434812 Anomaly 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 memory Electricity 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. Final 2023-05-29T09:05:58Z 2023-05-29T09:05:58Z 2021 Conference Paper 10.1109/AiDAS53897.2021.9574184 2-s2.0-85118992042 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118992042&doi=10.1109%2fAiDAS53897.2021.9574184&partnerID=40&md5=e5387eec6d85d02a12bf289409bb5fdb https://irepository.uniten.edu.my/handle/123456789/25997 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	Anomaly 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 memory
author2	54946009300
author_facet	54946009300 Salleh N.S.M. Saripuddin M. Suliman A. Jorgensen B.N.
format	Conference Paper
author	Salleh N.S.M. Saripuddin M. Suliman A. Jorgensen B.N.
spellingShingle	Salleh N.S.M. Saripuddin M. Suliman A. Jorgensen B.N. Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
author_sort	Salleh N.S.M.
title	Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
title_short	Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
title_full	Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
title_fullStr	Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
title_full_unstemmed	Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory
title_sort	electricity anomaly point detection using unsupervised technique based on electricity load prediction derived from long short-term memory
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
_version_	1806428130865315840
score	13.222552

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

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