Energy big data: A survey <s> I. INTRODUCTION <s> A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected. <s> BIB001 </s> Energy big data: A survey <s> I. INTRODUCTION <s> In daily life, people tend to use mobile networks for more accurate overall data. With intelligent mobile devices, almost all kinds of data can be collected automatically, which contributes directly to the blooming of eHealth. However, large amounts of data are also leading us into the era of big data, in which new data collection, transmission, and processing techniques are required. To ensure ubiquitous data collection, the scale of mobile eHealth networks has to be expanded. Also, networks will face more pressure to transmit large amounts of eHealth data. In addition, because the processing time increases with data volume, even powerful processors cannot always be regarded as efficient for big data. To solve these problems, in this article, an interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed. In this three-layer architecture, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer. Then the data integrating layer checks the accuracy of data, and also prepares for data processing. In the end, an RVNS queue is adopted for rapid data processing in the data analyzing layer. After processing with relevant rules, only valuable data will be reported to health care providers, which saves their effort to identify these data. Simulation shows that IRQA is steady and fast enough to process large amounts of data. <s> BIB002
Smart grid is a major research and development direction in today's energy industry. It refers to the next generation power grid which integrates conventional power grid and management of advanced communication networks and pervasive computing capabilities to improve control, efficiency, reliability and safety of the grid  . Smart grid delivers electricity between suppliers and consumers so as to form a bidirectional electricity and information flow infrastructure. It fulfills the demands of each stakeholder, functionality coordination in electric power generation, terminal electricity consuming and power market. It also improves the efficiency in each part of the system operation and reduces the cost and environmental impact. At the same time, the system reliability, self-healing ability and stability are improved BIB001 . Integrating telecommunication, automation and electric network control, smart grid requires reliable real-time data processing. To support this requirement and benefit future analysis and decision making, huge amount of historic data should be well fetched and stored in a reasonable time budget. In addition, there are various sources that huge amount of data can be generated through diverse measurements acquired by Intelligent Electronic Devices (IEDs) in the smart grid: • Data from power utilization habits of users; • Data from Phasor Measurement Units (PMUs) for situation awareness; • Data from energy consumption measured by the widespread smart meters; • Data from energy market pricing and bidding collected by Automated Revenue Metering (ARM) system; • Data from management, control and maintenance of device and equipment in the electric power generation, transmission and distribution in the grid; • Data from operating utilities, like financial data and large data sets which are not directly obtained through the network measurement. All aforementioned sources increase the grid's data volume. According to the investigation in BIB002 , by 2009 the amount of data in electric utilities' system has already reached the level of TeraBytes (TBs). With every passing day, more TBs of data are emerging at infrastructure company data centers. This fact accelerates the pace of big data technology applying to the smart grid area. To obtain a better understanding of the big data application on smart grid, we give an overview of energy big data (big data applied in the energy domain) covering the recent researches and development in the context of an integrated architecture, key enabling technologies, security, typical applications and challenges. The remainder of this survey is organized as follows. Overview of the energy big data is provided in Section II, and related analytics frameworks are presented in Section III. In Section IV, key enabling technologies are listed. Section V illustrates the security issues and Section VI looks into main application areas. Challenges are discussed in Section VII and Section VIII concludes this paper.