LAUSR

Energy theft and defective meters not only lead to non-technical losses (NTLs) that are extremely detrimental to energy distributors and power infrastructure, but NTLs are also a major cause of damages to electricity and massive losses of revenue each year. Automatic meter reading (AMR) is a system used by the Provincial Electricity Authority (PEA) of Thailand for NTLs detection that works in conjunction with physical inspection. At present, using only AMR is unable to classify and identify the types of abnormalities that occur. In addition, another important issue that has been rarely studied and should not be neglected is the balancing of data. Because this issue has a negative impact on minorities, learners misclassify and leads to incorrect predictions. This paper proposes unbalanced data techniques for classifying energy theft, defective and normal customers based on AMR monitoring of PEA. In order to handle the multiclass imbalance problems, three methods were evaluated and compared: anomaly models (AM), adaptive synthetic sampling (ADASYN), and image data augmentation (IA). The data were extracted from time series into an image using a recurrence plot (RP) and classified as abnormal patterns of imaging time series using six deep learning models: LeNet5, AlexNet, VGGNet19, DenseNet121, ResNet50, and InceptionV3. The experimental results demonstrate that data generation techniques using anomaly models and DenseNet121 for classifying provided the best results compared to other techniques, and data extraction using images yields better results than time series. Moreover, compared to balanced and unbalanced data, classification evaluation using AUC-ROC and F1-score are the most appropriate evaluation methods, and importantly, balancing the data before classification improved the model performance.