Natural smart home automation system using LSTM based on household behaviour

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.