Implementation of Big Data and Machine Learning in Smart Grid with Correlated Safety Considerations: Review

This report provides a systematic analysis of the implementation of massive information and machine learning in the electric energy grid, driven by the advent of the Smart Grid (SG), the succeeding energy generation infrastructure. Interconnection is established at the center of this modern grid network that the internet of things (IOT) offers. This connection and the frequent interaction needed in this framework also imposed a significant amount of information that requires methodology much preferable to traditional techniques for detailed evaluation and decision formation. The IOT-integrated SG framework can include cost-effective load prediction and information-gathering strategies. Large data processing and machine learning approaches are important to gain those advantages. Cybersecurity becomes a critical problem in SG's complex connected system; IOT machines and their information transform into big victims of threats. This document contains certain security breaches and their approaches. Essential expertise gathered across the literature review is aggregated in the subsequent parts to offer a simple description and the results of this comprehensive analysis are described to deliver a succinct perspective of this area of study and encouraging potential sectors of scholarly and technological development, with inherent restrictions along with feasible alternatives and their efficiency.