The Role of Data Analysis in the Development of Intelligent Energy Networks

TLDR

In intelligent energy networks, the proliferation of smart meter data necessitates data analysis for clustering, forecasting, optimization, pricing, monitoring, and diagnostics, employing techniques such as pattern recognition, machine learning, data mining, and statistics. The article reviews how data analysis methods are applied to energy big data and argues that current techniques are insufficient, calling for more comprehensive methods to handle growing data volumes and extract valuable insights. The authors conduct a review of data analysis techniques applied to energy big data, evaluating their suitability for intelligent energy networks.

Abstract

Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, and so on. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by IENs, therefore more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.