Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids

Aug 2, 2026

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The proliferation of Internet of Things (IoT) devices in smart grid infrastructure generates massive sensor datasets, creating both opportunities and operational risks that require advanced analytics. The document investigates machine learning–based anomaly detection to enable predictive maintenance, aiming to identify irregular patterns that indicate likely equipment failures. It reviews algorithms such as Isolation Forest, One-Class SVM, Autoencoders, and Random Forest, and discusses performance evaluation, model selection, integration, and deployment challenges. It also characterizes anomaly categories and critiques gaps in existing studies within smart grids.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Predictive maintenance enabled by anomaly detection

Challenges in monitoring smart grid assets

Literature Review

Importance of predictive maintenance in smart grids

Proactive approach to maintenance

Minimizing operational disruptions

Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids Edwin Omol 1*, Lucy Mburu2, Paul Abuonji2, Dorcas Onyango3 1 Department of Computing and Information Technology, Kenya Highlands University P. O. Box 123 – 20200 Kericho, Kenya 2 School of Technology, KCA University P. O. Box 56808 – 00200 Nairobi, Kenya 3 School of Business, KCA University P. O. Box 56808 – 00200 Nairobi, Kenya *Corresponding Author: Email: [omoledwin@gmail.com](omoledwin@gmail.com) 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 presentsan 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. Keywords: Internet of Things (IoT), Predictive Maintenance, Anomaly Detection, Machine Learning Algorithms and Smart Grids. I. INTRODUCTION The rapid proliferation of Internet of Things (IoT) devices in the energy sector has ushered in a new era of data-driven decision-making for smart grid management. These interconnected sensors generate an enormous volume of real-time data, providing unprecedented insights into the performance and health of the smart grid infrastructure [1] . However, with the ever-growing complexity and scale of these systems, the task of monitoring and maintaining the vast array of devices poses significant challenges. In this context, anomaly detection using machine learning techniques has emerged as a promising approach to proactively address potential equipment malfunctions and optimize maintenance strategies [2,5].Predictive maintenance, enabled by anomaly detection, offers a proactive and intelligent way to manage smart grid assets. Traditional maintenance approaches, often reactive and time-based, have proven to be inefficient and costly, leading to unplanned downtime and operational disruptions [3] . In contrast, predictive maintenance leverages the power of advanced data analytics to detect anomalies in real-time sensor data [4] . By identifying deviations from normal patterns and predicting potential equipment failures, utilities can schedule maintenance activities strategically, minimize downtime, and optimize resource allocation. The integration of machine learning techniques for anomaly detection in IoT sensor data holds immense potential in revolutionizing the way we maintain and operate smart grids [6] . By harnessi

FAQs

Why is anomaly detection important for predictive maintenance in smart grids?

Because utilities can detect deviations from normal sensor patterns in real time and anticipate potential equipment failures, enabling timely and strategic maintenance rather than reactive repairs.

Which machine learning algorithms are discussed for IoT sensor anomaly detection?

The document reviews Isolation Forest, One-Class SVM, Autoencoders, and Random Forest, evaluating their ability to identify anomalies in large-scale data streams.

What aspects are considered when selecting and evaluating anomaly detection models?

It covers performance evaluation and model selection techniques for IoT sensor data, along with criteria used for algorithm selection to support reliable anomaly identification.

Aug 2, 2026

Upload by Rowan