AI-driven anomaly detection in cloud-managed renewable energy platforms

The rapid expansion of renewable energy systems has contributed to the extensive use of cloud-based monitoring applications that receive and process massive amounts of operational telemetry information. Nevertheless, the dynamic and complex nature of renewable energy systems makes it difficult to detect abnormal behaviors that can adversely impact the reliability and efficiency of energy generation. This paper proposes an artificial intelligence-based anomaly detection system designed for cloud-controlled renewable energy systems. The proposed solution integrates deep representation learning models with a contextual drift-based anomaly scoring mechanism to identify anomalous operation patterns in multivariate renewable energy telemetry data. Normal system behavior is learned using an autoencoder-based architecture, and the proposed Context-Aware Residual Drift Scoring (CARDS) algorithm enhances anomaly detection by identifying contextual anomalies in system performance. Experimental evaluation was conducted on multivariate renewable energy telemetry data under cross-validation setups consistent with those used for comparison models. The proposed framework achieved a detection accuracy of 97.4% and an ROC–AUC score of 0.98, outperforming baseline algorithms such as Isolation Forest, LSTM-based detection, and Transformer-based models. These findings demonstrate that the suggested AI-based system offers a viable and scalable approach to enhancing reliability and operational intelligence in cloud-based renewable energy monitoring systems.