AI-Based Renewable Energy Asset Monitoring System Using Time-Series Forecasting and Anomaly Detection

This research successfully demonstrates the implementation of an intelligent, AI-driven framework for the real-time monitoring and predictive analysis of solar photovoltaic (PV) assets. By integrating Internet of Things (IoT) telemetry with advanced machine learning architectures, the study addresses the critical limitations of traditional, reactive monitoring systems. The empirical results confirm that the Long Short-Term Memory (LSTM) model provides high-precision energy forecasting, effectively capturing the stochastic nature of solar irradiance and power output. Furthermore, the deployment of the Isolation Forest algorithm and neural network classifiers proved highly effective in autonomous anomaly detection, allowing for the early identification of technical faults and performance degradation. The synergy between these AI models and the real-time visualization dashboard provides operators with a powerful decision-support tool, shifting the maintenance paradigm from reactive repairs to cost-effective, proactive prevention. While the current study relies on high-fidelity simulated IoT data and historical datasets, it establishes a robust foundation for large-scale renewable energy management. Future research will focus on the deployment of this framework onto physical hardware edge devices to evaluate performance under diverse, real-world environmental stressors. Additionally, the modular nature of this architecture allows for future expansion into hybrid energy systems, contributing significantly to the global transition toward reliable, data-driven, and sustainable smart grids

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