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