The Role of Data Analysis in the Development of 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, etc. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by the IENs and, therefore, more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.

💡 Analysis

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, etc. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by the IENs and, therefore, more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.

📄 Content

The Role of Data Analysis in the Development of Intelligent Energy Networks Zhanyu Ma1, Jiyang Xie1, Hailong Li2,3,, Qie Sun4,, Zhongwei Si5, Jianhua Zhang6, Jun Guo1

1. Pattern Recognition and Intelligent Systems Lab., Beijing University of Posts and Telecommunications, Beijing, China.
2. School of Business, Society and Engineering, Mälardalen University, Västerås, Sweden 3.Tianjin Key Laboratory of Refrigeration Technology, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin, China
3. Institute of Thermal Science and Technology, Shandong University, Ji’nan, China 5.Key Laboratory of Universal Wireless Communications, MOE, Beijing University of Posts and Telecommunications, Beijing, China 6.State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 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, etc. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by the IENs and, therefore, more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information. Index Terms Intelligent energy networks (IENs), data analysis, smart grids, smart district heating networks, smart natural gas networks I. INTRODUCTION The intelligent energy networks (IENs), which cover smart grids, smart district heating (DH) networks, and smart natural gas (NG) networks, can be defined as networks that intelligently optimize energy exchange based on bilaterally sharing information from both producers and consumers, and also refer to the integration of advanced information and communication technologies (ICT) with conventional energy networks [1]. In IENs, networking, ICT and data analysis methods are integrated into every aspect of the energy system including energy generation, energy transmission, energy distribution and consumer appliances [2]. The IENs have developed rapidly in recent years in order to meet the increasing demand for energy delivered in a robust, flexible, environmentally friendly and cost effective way [1]. The developmental stages of IENs are illustrated in Figure 1, which illustrates that the trend follows that of the development of smart meters. Smart meters are electronic devices that measure energy consumption and operate two-way communication regarding billing information and the status of energy systems. The capability of operating two-way communication is the most important feature that distinguishes smart meters from conventional meters. The evolution of smart meters has resulted in a rapid increase in data about energy systems. For example, the increase in meter reading frequency from once a month to every 15 minutes yields about 3,000 times more data. The large volume of data opens up new opportunities for a better understanding of consumer behaviour, clustering energy consumption patterns, and further optimizing energy production and distribution.

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Fig. 1: Stages of development of an energy network. With the rapid development of energy networks, data analysis becomes particularly important and essential to provide the foundation for IENs. Traditional energy networks can only collect local statistics from a small amount of data. However, they cannot efficiently handle the huge volume of data generated in IENs. Although manually collecting and analyzing data are acceptable in traditional energy networks, an advanced system dependent on modern computer technology can not only automatically collect data, process data, and deliver results, but also analyze the results against expected demand, standards, and concurrent data in IENs [3]. With the development of IENs, more and more data can be collected from energy networks, thus requiring the development of robust and efficient data analysis methods. The role of data analysis in IENs is illustrated in Figure 2. Data analysis can extract valuable information from a large amount of data, which can then be used for model design and algorithm implementation [2]. Many data analysis methods, which include pattern recognition and machine learning, have been developed and adopted mainly in three areas in IENs: i) malfunction diagnosis of energy networks to identify locations of current faults and to forecast locati

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