Abstract

The growing electricity demand, coupled with challenges such as energy wastage, biased billing in multi-unit buildings, and the absence of adequate predictive energy management, necessitates intelligent solutions. This paper presented the development of a smart energy system tailored for multi-unit residential buildings. By integrating IoT technology with a trained LSTM machine learning model, the system enabled real-time energy monitoring, control, and hourly prediction of energy consumption. Core components include dual PZEM004T sensors, an ESP32 microcontroller, a keypad, an LCD, and relays, all managed via the Blynk IoT platform. The system performed key functions such as threshold-based relay switching, overvoltage and overcurrent protection, and AI-powered forecasting. Results demonstrated high accuracy in monitoring, responsive control through local and remote interfaces, and effective prediction with a low Mean Squared Error (MSE) of 0.0229. The solution ensured fair energy billing, reduced waste, and supported sustainable energy practices.

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