Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids
Abstract
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 the
application 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.
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