CNN-Based Smart Sensor Networks for Real-Time Energy Monitoring and Fault Diagnosis

The rising demand for efficient energy management has rendered the incorporation of deep learning techniques in smart sensor networks essential. This research presents a CNN-based intelligent sensor network for real-time energy monitoring and problem diagnostics, utilizing convolutional neural networks (CNNs) to improve accuracy and dependability. The model is engineered to analyze sensor data in real-time, identifying anomalies and enhancing energy efficiency in industrial and smart grid contexts. The proposed system is implemented using the UCI Energy Efficiency dataset, an online repository containing real-world energy consumption and fault patterns. The simulation platform incorporates TensorFlow and NS-3 for network analysis, guaranteeing comprehensive evaluation. The performance evaluation indicates that the suggested CNN-based model attains an accuracy of 97.8%, precision of 97.5%, recall of 97.6%, and an F1-score of 97.6%, surpassing conventional machine learning methods like SVM (92.3% accuracy) and Random Forest (94.5% accuracy). The technology decreases false positives by 25% relative to current methodologies. The application domain encompasses renewable energy systems, industrial automation, and power grid optimization, whereby real-time problem detection is essential. The primary benefits of this system encompass higher fault detection precision, diminished false positives, and increased system efficiency, ultimately resulting in reduced operational expenses and sustainable energy use.