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October 3, 2025Open Access

Intelligent IoT Framework for Power Management and Predictive Maintenance in Critical Infrastructure

Key Points

The system demonstrates a 25% improvement in equipment uptime compared to traditional approaches, enhancing reliability.
Analysis using machine learning algorithms, such as Decision Tree and Neural Networks, predicts equipment failures effectively.
Integration of IoT sensors enables real-time monitoring of essential variables like voltage and temperature to optimize performance.
The scalable solution significantly reduces energy costs by 30%, promoting financial efficiency in critical settings.

Abstract

Reliable power supply is essential in critical environments such as hospitals and industrial facilities, where outages can pose severe risks. This study presents an IoT-enabled intelligent framework for automated power management and predictive maintenance. The system integrates IoT sensors to monitor voltage, current, temperature, and vibration in real time, while machine learning algorithms (Decision Tree, Random Forest, and Neural Networks) analyze the data to predict equipment failures and optimize resource allocation. The framework also ensures seamless switching between power sources (mains, generators, and solar), guaranteeing uninterrupted supply to critical equipment. Experimental results demonstrate a 25% improvement in equipment uptime and a 30% reduction in energy costs compared to conventional systems. This scalable and reliable solution enhances operational resilience, reduces maintenance costs, and strengthens power management in critical infrastructure. Keywords: IoT, Power Management, Machine Learning, Predictive Maintenance, Critical Infrastructure

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