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Ahmad Po’ad, Farhana
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Natural smart home automation system using LSTM based on household behaviour Susantok, Mochamad; Ahmad Po’ad, Farhana; Joret, Ariffuddin; Hilwa Salsabillah, Maulina
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp758-770

Abstract

A smart home automation system (SHAS) utilizing data-driven learning is an advanced internet of things (IoT) application aimed to learn household behavior to prevent miniatur circuit breaker (MCB) trips due to overload. Unlike traditional deterministic methods, this study leverages a layered AI model, featuring real-time data collection, long short-term memory (LSTM) based learning, and an automatic control system. The LSTM classification model generates precise ON/OFF control signals sent to IoT smartplugs, optimizing appliance usage and reducing the risk of electrical overload. Data from smartplug sensors, including appliance status and environmental factors like power consumption, temperature, and humidity, were collected every minute over three months, yielding 80,818 data points. The system's performance was evaluated on three appliances: Air Conditioner, Television, and Water Pump Machine. Results showed high accuracy for Television at 98% and Water Pump Machine at 97.6%, with slightly lower accuracy for Air Conditioner at 81.9%. This demonstrates the system's effectiveness in real-world applications. The scalability and adaptability of the Natural SHAS model to different appliances and environments mark a significant advancement in smart home automation, offering a practical solution for preventing electrical overload and improving household energy management.
Co-Authors Hilwa Salsabillah, Maulina Joret, Ariffuddin Mochamad Susantok