An initial study of predictive machine learning analytics on large volumes of historical data for power system applications

Abstract

Nowadays large volumes of industrial data are being actively generated and collected in various power system applications. Industrial Analytics in the power system field requires more powerful and intelligent machine learning tools, strategies, and environments to properly analyze the historical data and extract predictive knowledge. This paper discusses the situation and limitations of current approaches, analytic models, and tools utilized to conduct predictive machine learning analytics for very large volumes of data where the data processing causes the processor to run out of memory. Two industrial analytics cases in the power systems field are presented. Our results indicated the feasibility of forecasting substations fault events and power load using machine learning algorithm written in MapReduce paradigm or machine learning tools specific for Big Data.