International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Edge Intelligence for Energy Analysis: A Real-Time IoT-Embedded Framework for Proactive Load Management |
|---|---|
| ABSTRACT | The rising global energy demand, along with the pressing need for sustainable approaches, calls for a paradigm shift from traditional energy management to intelligent and data-driven solutions. This paper describes a holistic IoT-enabled smart embedded system for real-time energy management and optimization. The proposed solution combines edge computing architecture with sophisticated sensor networks to support localized processing, thereby minimizing latency and bandwidth utilization. Our system uses a multi-layered architecture with perception, network, edge processing, and application layers to support seamless data transfer from diverse IoT devices to end applications. By incorporating the use of lightweight machine learning models such as an optimized Long Short-Term Memory (LSTM) network for load forecasting and a reinforcement learning model for demand response, the system performs predictive optimization of energy consumption behavior. Experimental validation shows dramatic improvements of 32.7% in peak energy demand, 28.4% in operational cost, and 41.2% in renewable energy resource utilization compared to traditional centralized systems. Additionally, the embedded security solution using elliptic curve cryptography and blockchain-based integrity validation provides comprehensive protection against cyber-attacks while ensuring data privacy. The proposed solution provides a scalable, resilient, and intelligent solution for energy management, filling important gaps in the current state of the art and providing a basis for sustainable smart grid deployment in residential, commercial, and industrial sectors. |
| AUTHOR | AKILANDESWARI R, HEMALATHA R Assistant Professor, Department of Computer Science and Electronics, Sindhi College, Bengaluru, India Associate Professor, Department of Computer Science and Electronics, Sindhi College, Bengaluru, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402021 |
| pdf/21_Edge Intelligence for Energy Analysis A Real-Time IoT-Embedded Framework for Proactive Load Management.pdf | |
| KEYWORDS | |
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