Real-Time Anomaly Detection in IoT-Enabled Solar Tracking Systems Using Neural Networks
Indra Kishor, Udit Mamodiya, Bright Keswani · Advances in information security, privacy, and ethics book series · 2025
The process of the integration of Internet of Things (IoT) technology with solar tracking systems has disrupted energy generation through full-time monitoring and optimization, combined with data-driven decision-making. Solar tracking systems would trigger a dynamic photovoltaic orientation in panels to maximize energy capture; however, operational inefficiencies, system vulnerabilities, and maintenances curb their operational power. Ably, the paper delineates a neural network-based framework for real-time anomaly detection in IoT-enabled solar tracking systems with a deep dive into other advanced architectures such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to process sensor data relative to energy output, actuator activity, and other environmental conditions to attain and address abnormal operations comfortably.