Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Tech science press
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.
Description
Keywords
Energy consumption; intelligent; machine learning; technique;
smart homes; prediction
Citation
ALZAABI, HANADI OBAID (2021) Intelligent Energy Consumption for Smart Homes using Fused Machine Learning Technique. dissertation. The British University in Dubai (BUiD).