Open Access

ARTICLE

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Hanadi AlZaabi¹, Khaled Shaalan¹, Taher M. Ghazal^2,3,*, Muhammad A. Khan^4,5, Sagheer Abbas⁶, Beenu Mago⁷, Mohsen A. A. Tomh⁶, Munir Ahmad⁶

1 Faculty of Engineering and IT, The British University in Dubai, United Arab Emirates
2 Center for Cyber Security, Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia
3 School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates
4 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan
5 Pattern Recognition and Machine Learning Lab., Department of Software, Gachon University, Seongnam, Gyeonggido, 13120, Korea
6 Faculty of Computer Science, NCBA&E, Lahore, 54660, Pakistan
7 School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates

* Corresponding Author: Taher M. Ghazal. Email:

Computers, Materials & Continua 2023, 74(1), 2261-2278. https://doi.org/10.32604/cmc.2023.031834

Received 28 April 2022; Accepted 12 July 2022; Issue published 22 September 2022

Abstract

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents’ actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.

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Keywords

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