A comparative analysis of deep neural network models in IoT‐based smart systems for energy prediction and theft detection

Abstract

Traditional analog and digital meters are being substantially replaced with technological advances and the Internet of Things (IoT) introduction. The smart meter is highly preferred for accessing real‐time consumption, tariff calculation, and remote system control. These smart meters also prevent the majority from bypassing theft. Despite its intelligence, it cannot be 100% secure. An error in the readings can be caused by hacking or damage to meter components making the utility companies suffer significant losses. Based on past and future energy consumption data predictions on the consumer side, various methods are used in the proposed work to identify theft or anomalies in smart meter readings. Forecast‐based detection proved to be the most effective and accurate method. The primary and secondary decision models, which employ a variety of statical analyses to identify system anomalies, serve as the foundation for the energy consumption that follows the forecasting. Past 24‐h data is needed for forecasting, which is passed through different statical calculations such as RMSE, simple moving average, and Absolute Percentage Error to conclude detecting the normal values. Long short‐term memory gives high accuracy of 97% for forecasting and detecting abnormalities.