Anomaly detection method based on convolutional neural network for power consumption

As economic and social progress accelerates, household incomes are rising steadily, leading to a growing proliferation of electronic devices in homes. How to extract abnormal electricity usage information from these massive amounts of data, in order to provide decision-making basis for users to timely detect equipment abnormalities and reduce energy consumption losses, is of great significance for achieving energy-saving and emission reduction goals. This study develops a CNN (Convolutional Neural Network) based framework for detecting and analyzing anomalous power usage patterns, aiming to enhance the reliability and operational efficiency of electrical grids. This research primarily investigates power grid consumption patterns, leveraging CNN's nonlinear modeling and feature extraction advantages to develop a specialized framework for anomaly detection in energy usage. The proposed system's reliability and practical applicability are demonstrated through comprehensive empirical validation using large-scale datasets.