The Experts below are selected from a list of 73545 Experts worldwide ranked by ideXlab platform
Kaile Zhou - One of the best experts on this subject based on the ideXlab platform.
-
Data quality of electricity consumption data in a smart Grid Environment
Renewable and Sustainable Energy Reviews, 2017Co-Authors: Wen Chen, Kaile Zhou, Shanlin Yang, Cheng WuAbstract:With the increasing penetration of traditional and emerging information technologies in the electric power industry, together with the rapid development of electricity market reform, the electric power industry has accumulated a large amount of data. Data quality issues have become increasingly prominent, which affect the accuracy and effectiveness of electricity data mining and energy big data analytics. It is also closely related to the safety and reliability of the power system operation and management based on data-driven decision support. In this paper, we study the data quality of electricity consumption data in a smart Grid Environment. First, we analyze the significance of data quality. Also, the definition and classification of data quality issues are explained. Then we analyze the data quality of electricity consumption data and introduce the characteristics of electricity consumption data in a smart Grid Environment. The data quality issues of electricity consumption data are divided into three types, namely noise data, incomplete data and outlier data. We make a detailed discussion on these three types of data quality issues. In view of that outlier data is one of the most prominent issues in electricity consumption data, so we mainly focus on the outlier detection of electricity consumption data. This paper introduces the causes of electricity consumption outlier data and illustrates the significance of the electricity consumption outlier data from the negative and positive aspects respectively. Finally, the focus of this paper is to provide a review on the detection methods of electricity consumption outlier data. The methods are mainly divided into two categories, namely the data mining-based and the state estimation-based methods.
-
Optimal load distribution model of microGrid in the smart Grid Environment
Renewable and Sustainable Energy Reviews, 2014Co-Authors: Kaile Zhou, Shanlin Yang, Zhiqiang Chen, Shuai DingAbstract:Abstract In the smart Grid Environment, the flexible and diverse distributed generation (DG) and microGrid (MG) are attracting considerable attention. There are many key management and optimization issues involved in smart Grid. As an important part of smart Grid optimizations, the optimal load distribution of MG contributes to the efficient operation of MG in the smart Grid Environment. However, traditional optimal load distribution models of large-scale power generation systems are not fully applicable to MG for their distinct characteristics. In this paper, we first introduce the MG in smart Grid and analyze its characteristics. Then, we present a review of the optimal load distribution models of MG in the smart Grid Environment, and point out the deficiencies of the existing models. Finally, a comprehensive optimal load distribution model of MG both in objective functions and constraints is established and discussed.
-
a review of electric load classification in smart Grid Environment
Renewable & Sustainable Energy Reviews, 2013Co-Authors: Kaile Zhou, Shanlin Yang, Chao ShenAbstract:The load data in smart Grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart Grid Environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented.
Alexandru Stefanov - One of the best experts on this subject based on the ideXlab platform.
-
ISGT - Cyber-power system security in a smart Grid Environment
2012 IEEE PES Innovative Smart Grid Technologies (ISGT), 2012Co-Authors: Alexandru StefanovAbstract:Smart Grid heavily relies on Information and Communications Technology (ICT) to manage the energy usage. The concept of smart Grid implies the use of “smart” devices, such as smart meters or Remote Terminal Units (RTUs), that require extensive information to optimize the power Grid. As the communication network is based on TCP/IP and Ethernet technology, new cyber vulnerabilities are introduced that can be exploited by malicious attackers. Cyber security has become a serious concern due to various intrusion incidents. Cyber attacks can make a significant impact on the Grid, which will involve not only steady-state but also dynamic behaviors. A cyber-power system approach has been established that explicitly models the interaction between ICT and the power system. New technologies are under development to enhance the ICT vulnerability assessment and evaluate the impact of cyber attacks on system operation. This paper presents the cyber security issues in a smart Grid Environment and cyber attack/mitigation scenarios using a testbed at University College Dublin (UCD).
Shanlin Yang - One of the best experts on this subject based on the ideXlab platform.
-
Data quality of electricity consumption data in a smart Grid Environment
Renewable and Sustainable Energy Reviews, 2017Co-Authors: Wen Chen, Kaile Zhou, Shanlin Yang, Cheng WuAbstract:With the increasing penetration of traditional and emerging information technologies in the electric power industry, together with the rapid development of electricity market reform, the electric power industry has accumulated a large amount of data. Data quality issues have become increasingly prominent, which affect the accuracy and effectiveness of electricity data mining and energy big data analytics. It is also closely related to the safety and reliability of the power system operation and management based on data-driven decision support. In this paper, we study the data quality of electricity consumption data in a smart Grid Environment. First, we analyze the significance of data quality. Also, the definition and classification of data quality issues are explained. Then we analyze the data quality of electricity consumption data and introduce the characteristics of electricity consumption data in a smart Grid Environment. The data quality issues of electricity consumption data are divided into three types, namely noise data, incomplete data and outlier data. We make a detailed discussion on these three types of data quality issues. In view of that outlier data is one of the most prominent issues in electricity consumption data, so we mainly focus on the outlier detection of electricity consumption data. This paper introduces the causes of electricity consumption outlier data and illustrates the significance of the electricity consumption outlier data from the negative and positive aspects respectively. Finally, the focus of this paper is to provide a review on the detection methods of electricity consumption outlier data. The methods are mainly divided into two categories, namely the data mining-based and the state estimation-based methods.
-
Optimal load distribution model of microGrid in the smart Grid Environment
Renewable and Sustainable Energy Reviews, 2014Co-Authors: Kaile Zhou, Shanlin Yang, Zhiqiang Chen, Shuai DingAbstract:Abstract In the smart Grid Environment, the flexible and diverse distributed generation (DG) and microGrid (MG) are attracting considerable attention. There are many key management and optimization issues involved in smart Grid. As an important part of smart Grid optimizations, the optimal load distribution of MG contributes to the efficient operation of MG in the smart Grid Environment. However, traditional optimal load distribution models of large-scale power generation systems are not fully applicable to MG for their distinct characteristics. In this paper, we first introduce the MG in smart Grid and analyze its characteristics. Then, we present a review of the optimal load distribution models of MG in the smart Grid Environment, and point out the deficiencies of the existing models. Finally, a comprehensive optimal load distribution model of MG both in objective functions and constraints is established and discussed.
-
a review of electric load classification in smart Grid Environment
Renewable & Sustainable Energy Reviews, 2013Co-Authors: Kaile Zhou, Shanlin Yang, Chao ShenAbstract:The load data in smart Grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart Grid Environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented.
Erich Schikuta - One of the best experts on this subject based on the ideXlab platform.
-
CLUSTER - Performance analysis of parallel database sort operations in a heterogenous Grid Environment
2007 IEEE International Conference on Cluster Computing, 2007Co-Authors: Werner Mach, Erich SchikutaAbstract:In this paper an analytical comparison of the performance behavior of parallel flavors of the well-known Binary Merge Sort and Bitonic Sort algorithm in a Grid Environment is presented. To keep the analysis clear and focused we concentrate on a limited number of characteristic parameters and develop a concise but comprehensive analytical model both for a generalized multiprocessor framework and a simplified heterogeneous Grid Environment. We justify that a meaningful model can be built upon only three characteristic parameter sets, describing node processing performance, the I/O and the disk bandwidth, which are the parameters for the optimization the Grid workflow by a smart brokerage mechanism. Based on these results the paper proves that by a smart enhancement of the algorithms exploiting the specifics of the Grid the well-known results of Bitton et al. for a homogenous multi-processor architecture are invalidated and reversed for a heterogenous Grid Environment.
Li Hua - One of the best experts on this subject based on the ideXlab platform.
-
Information query based on semantic association in Grid Environment
Journal of Computer Applications, 2009Co-Authors: Li HuaAbstract:To meet the information query for the rapidly increasing resource information in Grid,a arithmetic of resource information query matching based on metadata semantic association in the discover system of Metadata Directory Service(MDS) was proposed,after the disadvantage of the information service system MDS in Grid Environment was analyzed.Experimental results show that the arithmetic not only can implement information query in Grid Environment,but also can improve efficiency of resource information query compared the information query based on directory and semantic association.
