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

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.

Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

“…Te presence of a healthcare issue is indicated by the fact that 202 samples are appropriately forecasted as negative. Even though a healthcare issue exists, seven (7) samples is wrongly forecasted as positive, indicating the absence of a healthcare issue.…”

“…An IoMT-based intelligent prediction [5] system for breast cancer is present empowered by deep learning. Prediction of diabetes and energy consumption with fused machine learning [6][7][8] techniques is also adopted for the prediction of human diseases in previous studies.…”