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Towards Energy Efficient Home Automation: A Deep Learning Approach.

Murad Khan1, Junho Seo1, Dongkyun Kim1

  • 1School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.

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This study introduces a smart home automation system using machine learning to reduce energy consumption. It analyzes energy patterns and forecasts loads to intelligently schedule appliance usage, optimizing energy efficiency for users.

Keywords:
Internet of Thingsenergy managementmachine learningsmart homes

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Area of Science:

  • Artificial Intelligence
  • Smart Home Technology
  • Energy Management

Background:

  • Home Automation Systems (HAS) are gaining traction due to advancements in wireless technologies like 5G and WiFi 6.
  • Key challenges in HAS include managing energy demands, appliance scheduling, real-time machine learning, and user interaction.
  • Energy wastage in smart homes is a significant concern, driven by user lifestyles.

Purpose of the Study:

  • To propose an automated system for controlling energy consumption in smart homes.
  • To address challenges in energy demand, appliance scheduling, and real-time machine learning within HAS.
  • To reduce energy wastage by adapting appliance operation to user lifestyles.

Main Methods:

  • A three-phase approach employing machine and deep learning techniques for smart home energy management.
  • Phase 1: Feature extraction and classification using 1-dimensional Deep Convolutional Neural Network (1D-DCNN) on historic energy data.
  • Phase 2: Load forecasting using Long-short Term Memory (LSTM) based on extracted features.
  • Phase 3: Development of a scheduling algorithm utilizing forecasted data to optimize appliance operational times.

Main Results:

  • The proposed system effectively automates smart home appliances to minimize energy consumption.
  • The system adapts to the lifestyle patterns of smart home users.
  • Simulation results using authentic datasets demonstrate significant energy efficiency improvements.
  • The system shows potential to meet energy demands without relying on additional renewable energy sources.

Conclusions:

  • The developed smart home automation system offers an efficient solution for energy management.
  • The integration of 1D-DCNN and LSTM models provides accurate energy pattern extraction and load forecasting.
  • The intelligent scheduling algorithm optimizes appliance usage, leading to substantial energy savings.
  • This approach presents a novel strategy for sustainable smart home energy consumption.