ABSTRACT
The demand for energy sources such as electricity is increasing as the population is increasing, which results in high billing costs and more energy consumption. More factors are resulting from these issues. For example, the decreased awareness from residents about how to save energy, especially kids and elderly people who forget about turning off home appliances and lights when they are not needed to be on. HARMS provide a smart solution through the concept of machine learning (ML) and recommendations, it will monitor power consumption, show recommendations and control home appliances based on the resident's behaviors, when they are willing to turn on the room light or any other home appliance and when to turn them off in order to enhance energy saving. HARMS will also track the inhabitant's usual and unusual behavior to take an action. We must note that due to this exceptional situation (Covid-19 Pandemic), HARMS may be done either using actual hardware, simulation, or both. The hardware parts will consist of microcomputer, motion, light, and current transformer sensors. The software parts will consist of a control system that collects data from sensors and monitors the power consumption, a database to store the collected data, appropriate algorithms for the recommender system, and an android application to interact with the residents. Regarding the simulation will consist of a web-based application to represent the home environment and the appliances, including the control and the recommender systems. This project will experiment at the College of Computer Sciences and Information Technology (CCSIT) at King Faisal University (KFU). © 2022 IEEE.