SMART METERING SYSTEM USING AI TECHNIQUES

Abstract

The economy of energy suppliers and countries may be significantly impacted by non-technical losses (NTLs) in the electrical distribution system, which mostly involve electrical theft. Non-technical losses include consumer dishonesty, unethical transmission line tapping, and hacking or tampering with energy meters. The smart meter, a crucial component of the smart grid, is anticipated to benefit several stakeholders on the economic, social, and environmental levels. Smart meter data analytics, which deals with data gathering, transmission, processing, and interpretation that benefits all stakeholders, is one of the crucial elements that will define the success of smart meters. An artificial neural network can be created in order to find and identify meter manipulation or energy theft. The outcomes can also be applied to larger real-world systems. Additionally, a communication network that satisfies the security requirements for smart grid communication must be properly chosen and implemented in order to deploy smart meters. This article discusses different problems and difficulties related to the development, implementation, use, and upkeep of the smart metering system. Catchphrases- NTL, losses detection, Smart meter, Advanced Metering Infrastructure, Automatic Meter Reading, Neural Networks, Communication Systems, Energy Consumption Profiles.