Chapter 8 Machine Learning Applications for the Smart Grid Infrastructure

Abstract

Smart Grids (SG) provide a bright future for improved grid dependability and effective energy management, and they are already undergoing a dramatic transformation in today's electric power system. In order to meet the fluctuating demands for electricity, a smart grid with a reliable infrastructure is essential. Due to the dynamic nature of this ongoing shift, a wide variety of cutting-edge approaches are needed to analyse the massive amounts of data being produced by different parts of the system. The multiple variables that impact smart grid stability make it difficult to make accurate predictions. SG is an emerging technology that can be used in tandem with Deep Learning (DL) to create a more distributed and intelligent energy paradigm and to integrate high intelligence into supervisory and operational decision-making. Methods for running SG systems using DL are outlined in this chapter. In the SG and power systems, DL can be used for load forecasting issues. We then demonstrate how federated learning, edge intelligence, and distributed computing are all making room for DL to flourish in SG. Future power grids can use the provided challenges and research horizons as a set of guides. Some methods that are frequently employed are Multilayer Neural Networks, Gated Recurrent Units, Recurrent Neural Networks, Long Short-Term Memory, Densely Connected Convolutional Networks, and Residual Networks; Support Vector Machines; Logistic Regression; Decision Trees; Random Forests; Gradient Boosted Trees; and Residual Networks. Prediction, cyber-attack, anomaly detection, and electricity theft are where these algorithms shine in the context of the smart grid. In this paper, we provide a high-level overview of the most practical deep learning techniques for building AI that is robust, accurate and secure.