Sistema de supervisión energética para entornos industriales mediante gemelo digital e inteligencia artificial

Abstract

This project develops and implements an intelligent environmental and energy monitoring system for the industry, aiming to optimize operational efficiency, ensure energy safety, and generate strategic information based on ISO 50001 standards. The hypothesis proposes that the integration of IoT sensors, an interactive 3D digital twin, and artificial intelligence enables more accurate management of energy and environmental resources, justifying its relevance compared to traditional monitoring methods. During the project execution, temperature, humidity, vibration, and electrical consumption sensors, ESP32 modules, real-time databases, and 3D modeling techniques in CAD and Omniverse software were employed, alongside AI algorithms for anomaly detection. Controlled tests were performed to validate measurement accuracy, digital twin synchronization, and system predictive capabilities. Results demonstrated an estimated 15% energy saving, reliable alert generation, and real-time visualization of operational conditions. It is concluded that the system provides an integral tool for decision-making, improves maintenance planning, and strengthens industrial efficiency and sustainability. Keywords: Intelligent monitoring, Digital twin, IoT sensors, Energy efficiency, Artificial intelligence