Authors : Obbu Chandra Sekhar; Aakashdeep; Arnav Tyagi; Gaurav Kumar

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/32h3npmp

DOI : https://doi.org/10.38124/ijisrt/25may1177

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Electricity theft continues to be a major concern in the power sector, leading to significant financial and operational setbacks. This paper presents an Internet of Things (IoT)-based electricity theft detection system en- hanced with machine learning capabilities. Smart energy meters equipped with sensors, microcontrollers, and wireless communication modules are deployed to monitor real-time power consumption. The collected data is transmitted to a cloud- based platform, where it is used to train a machine learning model for accurate anomaly detection. By learning typical usage patterns, the model improves the precision and reliability of theft identification. Upon detecting irregularities such as tam- pering or unauthorized usage, the system generates auto- mated alerts and enables remote intervention by authorized personnel. This approach enhances grid security, supports proactive loss prevention, and lays the ground- work for scalable, data-driven energy management. Fu- ture work includes the integration of blockchain for data integrity and further system resilience.

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