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Abstract Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.
Abdullah Bajahzar (Mon,) studied this question.
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