International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 1, February 2020, pp. 575~580 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i1.pp575-580  575 Journal homepage: http://ijece.iaescore.com/index.php/IJECE A survey of big data and machine learning Surender Reddy Salkuti Department of Railroad and Electrical Engineering, Woosong University, Republic of Korea Article Info ABSTRACT Article history: Received Apr 3, 2019 Revised Sep 12, 2019 Accepted Sep 27, 2019 This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper. Keywords: Big data Distribution systems Machine learning Microgrid Power and energy Smart grid Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Surender Reddy Salkuti, Department of Railroad and Electrical Engineering, Woosong University, 17-2, Jayang-Dong, Dong-Gu, Daejeon 34606, Republic of Korea. Email: surender@wsu.ac.kr 1. INTRODUCTION Artificial intelligence (AI) technologies can improve the conversation and cooperation of human and machine. These technologies are used for better interactions between human and machines. Now, we are living in a time of huge information, i.e., an age described by quick gathering of pervasive data. In numerous enterprises, it is developing and giving a way to enhance and streamline business. Numerous fields and segments, going from financial and business exercises to open organization, from national security to logical research in numerous territories, are associated with huge information issues [1]. Huge information has changed the world as far as anticipating client conduct. The introduction of huge information cannot abstain from specifying another current prominent term, interpersonal organizations and the connection between the two is self-evident, yet convoluted. During the last few years, wind, solar, hydro and nuclear power companies have greatly benefited from the power of AI, big data, machine learning and predictive models. They used these technologies to make better predictions, to increase their portfolio’s rate of return and to lower their costs [2]. AI came into picture in early fifties and sixties. It was mostly about enabling machines to do things on their own in programming machines which later increased into something like robotics and then in early 90s till 2010 we had this machine learning coming into picture where so many different kind of algorithms and approaches and different kind of theories were discovered and rented in order to begin machine to start learning on their own and then from 2010 onwards a new field which is a subset of artificial intelligence (AI), and machine learning (ML) is the subset of AI, and deep learning is a subset of ML which started in early 2010. ML is an interdisciplinary field which allows us to achieve some sort of AI by using statistical techniques [3].