<![CDATA[The increasing need for energy consumption monitoring has driven the development of systems capable of providing accurate electrical information and detecting disturbances at an early stage. This study aims to design an IoT-Based Energy Meter capable of monitoring electrical parameters in real time and detecting load anomalies as a basis for energy efficiency analysis. The system uses a PZEM-004T sensor and an ESP32 microcontroller to measure voltage, current, power, energy, and power factor (cos φ). The data is transmitted to an IoT platform via a wireless connection so it can be monitored remotely. A Long Short-Term Memory (LSTM) model is applied to identify normal power consumption patterns and detect deviations, while a rule-based method is used to detect critical conditions such as overcurrent. Test results show that the device is capable of performing measurements with high accuracy, with error percentages for voltage, current, power, and cos φ parameters ranging between 0%–5% for three types of loads: iron, electric fan, and refrigerator. The LSTM model also successfully detects anomalies such as power spikes, sudden current changes, and disconnected loads with a confidence level of 0.99–1.00. The integration of IoT, artificial intelligence, and basic protection systems results in a reliable and responsive monitoring device. In the future, this system has the potential to be developed for automatic efficiency analysis and intelligent load control.]]>
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