Context-Aware Energy Management Systems for Optimizing Power Consumption in Smart Ubiquitous Environments
Chittakula RohiniAssistant Professor, Department of Information Technology VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. rohini_ch@vnrvjiet.in; rohinichittakula1512@gmail.com0000-0001-8936-8730
Bichitra Singh NegiSchool of Engineering & Computing, Dev Bhoomi Uttarakhand University, Dehradun, Uttarakhand, India. dehradunce.bichitra@dbuu.ac.in0000-0002-3142-0062
Saksham SoodCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India. saksham.sood.orp@chitkara.edu.in0009-0004-4033-0159
Dr. Arvind Kumar PandeyAssociate Professor, Department of Computer Science & IT, ARKA JAIN University, Jamshedpur, Jharkhand, India. dr.arvind@arkajainuniversity.ac.in0000-0001-5294-0190
Dr. Prabhat Kumar SahuAssociate Professor, Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India. prabhatsahu@soa.ac.in0000-0002-0460-9783
Dr. Beemkumar NagappanProfessor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Karnataka, India. n.beemkumar@jainuniversity.ac.in0000-0003-3868-0382
Keywords: Smart Environment, Power Optimization, Sustainable Energy, Real-Time Analytics, Smart Grid, Energy Management.
Abstract
CAEMS are needed to optimize energy use in bright, ubiquitous environments. These environments combine multiple devices and sensors that are interconnected and require practical energy-use plans to minimize power consumption while still delivering high performance and user satisfaction. CAEMS uses the contextual information, including environmental factors, user behavior, device condition, and time, to dynamically optimize power consumption to be distributed where it is most desired. To process these types of systems in real-time data analysis using an adaptive algorithm to analyze energy savings across various situations. Which is used for to minimize the wastage among the sustainable energy consumptions. This solution not only improves the overall energy efficiency of bright spaces but also extends device lifecycles and lowers operating costs. The proposed model has the energy consumption efficiency rate as 80%, Context sensitivity as 70%, User comfort as 90%, Response time as 60% and System scalability as 75%. This paper discusses the architecture, principal components, and methodology of CAEMS, and how the approach is being used in future smart cities, IoT, and sustainable computing environments.