Authors : Dr. R. A. Burange; Ganesh Shingade; Piyush Gondane; Mrunali Besurkar; Sejal Paunikar; Aryan Patil

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/4c524wdc

Scribd : https://tinyurl.com/2rzxtket

DOI : https://doi.org/10.38124/ijisrt/26mar1246

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Industrial sectors account for a substantial portion of global electricity consumption, making the efficient monitoring and management of energy resources a critical requirement in today’s industrial landscape. With the continuous advancement of industrial automation and the steady rise in energy costs, organizations are increasingly seeking intelligent and scalable solutions to monitor, control, and optimize their power usage. This paper introduces the design and development of an Industrial Smart Energy Monitoring and Analytics System leveraging Internet of Things (IoT) technologies. The proposed system is capable of continuously tracking key electrical parameters such as voltage, current, power consumption, and temperature through advanced sensor modules interfaced with an ESP32 microcontroller. The acquired data is transmitted via wireless communication protocols to a cloud-based platform, where it is securely stored, processed, and analyzed in real time. A comprehensive and user-friendly dashboard is implemented to visualize energy consumption trends, generate alerts for abnormal or excessive usage, and provide actionable insights that assist industrial operators in improving energy efficiency and minimizing operational expenses. Additionally, the system enhances decision-making by enabling remote monitoring and data-driven analysis of industrial energy patterns. Experimental results indicate that the proposed solution delivers high accuracy, reliability, and scalability, making it well-suited for deployment in modern smart industrial environments. Future enhancements of the system may include the integration of machine learning techniques for predictive maintenance, anomaly detection, and automated energy optimization to further improve system intelligence and performance.

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