Document Details

Document Type : Thesis 
Document Title :
Machine Learning Based User Activity Analysis and Behavior Evaluation for More Energy Efficient Environments
تحليل نشاط المستخدم وتقييم سلوكه المبني على تعلم الالة لبيئات أكثر كفاءة في استخدام الطاقة
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : In recent years, energy consumption has considerably increased worldwide. One of the most notable reasons is the rapid increase of electronic and internet of things devices used in various forms into smart homes and smart appliances. However, such devices are not the merely cause of the large waste of energy, but rather how they are used by users in daily life. In this paper, we propose an approach to solve the wasteful energy consumption issue by analyzing the user side activities to enable addressing their adverse behaviors. Thus, different machine learning techniques are devoted for monitoring such users’ activities in an environment of interest to perceive, rate, and rank both activities and users upon three levels of energy consumers as green, balanced, and wasteful based on their observed interactions with electrical devices in that environment. Subsequently, a software architecture design can be accordingly recommended by which considers suggesting tips for the user to correct their wasteful behavior or shifting them from balanced level to better green saving level. As such, by encouraging them for becoming more energy conscious users, this in turn can make the environment, in which the proposed system is implemented, a more energy wise environment. 
Supervisor : Dr. Emad Sami Jaha 
Thesis Type : Master Thesis 
Publishing Year : 1444 AH
2023 AD 
Added Date : Thursday, April 27, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
عمر عبدالعزيز العرينانAlorainan, Omar AbdulazizResearcherMaster 

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