Machine learning-based monitoring of renewable energy systems using random forest and LSTM
The rapid expansion of renewable energy infrastructures has introduced significant challenges for monitoring system performance, ensuring regulatory compliance, and maintaining transparency in energy production and emissions reporting. Modern renewable energy systems generate large volumes of heterogeneous operational data through smart meters, sensor networks, supervisory control and data acquisition (SCADA) systems, and distributed generation platforms. Traditional monitoring approaches, which rely primarily on rule-based thresholds and periodic audits, often struggle to process such complex and dynamic data streams in real time. This paper proposes an artificial intelligence (AI)-driven intelligent monitoring framework designed to enhance operational oversight and sustainability monitoring in renewable energy systems. The proposed architecture integrates machine learning and anomaly detection techniques to analyze energy production data, detect abnormal operational patterns, and assess compliance with environmental and regulatory requirements. A design-science research methodology is adopted to develop and evaluate the framework using simulated renewable energy datasets representing solar and wind energy production scenarios. Experimental results demonstrate that the AI-based monitoring system significantly improves anomaly detection accuracy and reduces reporting delays compared with traditional rule-based monitoring methods. The proposed approach supports intelligent renewable energy infrastructure management by enabling proactive monitoring, improved operational transparency, and enhanced sustainability reporting.