Implementation on Machine Learning, Blockchain, and Decision Process for Securing Smart Grid

Abstract

Due to the increasing demand for electricity and rising energy costs, an intelligent energy monitoring system is implemented using IoT, Blockchain, and Machine Learning technologies. IoT hardware such as microcontrollers and electrical sensors is used to measure voltage, current, power, and energy consumption of appliances in real time. The collected data is transmitted to a server and securely stored using Blockchain technology, which ensures data integrity, transparency, and protection against tampering. This secure storage allows users to trust the recorded energy usage information. Machine Learning algorithms are applied to the stored energy data to analyze usage patterns, predict future energy consumption, identify inefficient appliances, and estimate monthly electricity expenses. The system provides a dashboard that displays real-time consumption, historical data, and predicted usage, helping users understand and optimize their energy consumption. The implemented solution demonstrates how the integration of IoT sensing, secure Blockchain storage, and intelligent data analysis can reduce energy wastage, lower electricity costs, and support sustainable energy management in residential and industrial environments.