A Predictive Tool For Grid Data Analysis Using Machine Learning Algorithms

Abstract

Power and energy sectors are collecting vast amount of data from different sources and trying to use computational tools to analyze and identify useful patterns in the data collected. Some of challenges observed with such big data are high volume, heterogeneous, and rapidly growing data. To efficiently handle such big data, machine learning algorithms are used. In this paper, such machine learning algorithms are used to predict patterns in the grid data collected from the Distributed Energy Resources (DER) at a local electrical engineering company. Predictive framework developed to preprocess the big data, classify the test and training datasets, and the application of different machine learning algorithms is discussed in this paper. The results obtained after analyzing the big data with different machine learning algorithms are also discussed in this paper.