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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM49709
Volume:
9 |
6 |
Jun 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Industrial sectors, such as manufacturing, chemical processing, and energy-intensive industries, face significant energy consumption costs and sustainability challenges. By implementing IPEMS, industries can monitor real-time energy consumption across various machines, identify inefficiencies, and predict equipment failures through advanced data analytics and machine learning algorithms. The system enables industries to make data-driven decisions regarding energy management, reducing both operational costs and the environmental footprint. IPEMS relies on key technologies, such as IoT-based sensors, smart meters, and cloud computing, to collect, analyse, and visualize energy usage data. The system offers actionable insights into the performance of equipment, suggesting optimization measures, such as load balancing, scheduling, and preventive maintenance. Additionally, predictive analytics enable the system to forecast potential energy inefficiencies and maintenance needs based on historical data patterns. With growing emphasis on green energy and sustainability in industrial operations, IPEMS helps organizations meet energy efficiency standards and regulatory requirements. By reducing unnecessary energy consumption, this system not only enhances profitability but also supports corporate sustainability goals by decreasing carbon emissions
KEYWORDS
Publication Date
6/9/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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