Sistem Monitoring Dan Prediksi Konsumsi Listrik Menggunakan Metode Long Short-Term Memory (LSTM) Berbasis Internet Of Things (IOT)

Abstract

The development of Internet of Things (IoT) technology enables real-time and efficient measurement and monitoring of electricity consumption. This study aims to design and develop an IoT-based electricity consumption monitoring and control system equipped with a prediction feature using the Long Short-Term Memory (LSTM) algorithm. The system uses the PZEM-004T sensor to measure electrical parameters such as voltage, current, power, and energy, which are then transmitted via the MQTT protocol using an ESP32 microcontroller. Electricity consumption data is displayed on a mobile application and stored in a Supabase database. In addition to monitoring features, the system also provides control over electrical devices through a relay, as well as user-configurable scheduling and consumption limit settings. The electricity consumption prediction feature is developed to provide estimated monthly bills or estimated time until prepaid electricity tokens run out, based on historical data. The implementation results show that the system is capable of performing real-time monitoring and control, as well as providing informative visualizations of consumption history in graphical form. This system is expected to help users manage their power consumption more wisely and efficiently.