Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids - Omol, Edwin; Mburu, Lucy; Onyango, Dorcas (International Journal of Science, Technology & Management, 2024)
The proliferation of Internet of Things (IoT) devices in the smart grid infrastructure has enabled the generation of massive amounts of sensor data. This wealth of data presents an opportunity to implement sophisticated data analytics techniques for predictive maintenance in smart grids. Anomaly detection using machine learning algorithms has emerged as a promising approach to identifying irregular patterns and deviations in sensor data, leading to proactive maintenance strategies. This article explores theapplication of machine learning techniques for anomaly detection in IoT sensor data to enable predictive maintenance in smart grids. We delve into various machine learning algorithms, including Isolation Forest, One-Class SVM, Autoencoders, and Random Forest, assessing their capabilities in identifying anomalies in large-scale data streams. The study also reviews the Performance Evaluation and Model Selection techniques for Anomaly Detection in IoT Sensor Data, possible integration and deployment challenges, and critique of the few selected studies. Explicitly, this scholarly inquiry questions the profound significance of predictive maintenance within the context of Smart Grids. It elucidates distinct categories of anomalies inherent within IoT Sensor Data.Furthermore, the article expounds upon various classes of Machine Learning Algorithms while also clarifying the criteria employed for their selection. Notably, the study probes the potential hindrances that could emerge during the deployment and integration of Machine Learning Techniques specifically aimed at Anomaly Detection in IoT Sensor Data. In addition, the research sheds light on the aspects that might have been inadvertently overlooked within the existing corpus of literature.