Anomaly Detection and Visualization for Electricity Consumption Data

Nyoungwoo Lee, Jehyun Nam, Ho-Jin Choi

Abstract: Power supplied enterprises need to accurately detect abnormal power consumption cases to predict power demand. Since actual abnormal power consumption patterns are irregular, a flexible model should be designed to address this situation. Thus, we inspect abnormal power consumption data and predict potential abnormal patterns. Based on these insights, the goal of this work is to generate data onto the identified abnormal patterns and to design a flexible model that can detect the generated abnormal data. As a result, a performance for anomaly detection of the final model recorded 74% and 72% accuracy for original abnormal and normal data, respectively, and randomly generated abnormal data recorded 95.07% accuracy for growth type and 89.69% accuracy for reduction type. We suggest a set of ways to identify potential abnormal data and design flexible models to address them.