The Experts below are selected from a list of 9464046 Experts worldwide ranked by ideXlab platform
Jun Guo - One of the best experts on this subject based on the ideXlab platform.
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the role of Data Analysis in the development of intelligent energy networks
arXiv: Other Computer Science, 2017Co-Authors: Jiyang Xie, Qie Sun, Jianhua Zhang, Jun GuoAbstract: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.
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The role of Data Analysis in the development of intelligent energy networks
IEEE Network, 2017Co-Authors: Zhanyu Ma, Jiyang Xie, Qie Sun, Zhongwei Si, Hailong Li, Jianhua Zhang, Jun GuoAbstract: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.
Anthony J. Onwuegbuzie - One of the best experts on this subject based on the ideXlab platform.
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Beyond Constant Comparison Qualitative Data Analysis: Using NVivo.
School Psychology Quarterly, 2011Co-Authors: Nancy L. Leech, Anthony J. OnwuegbuzieAbstract:The purposes of this paper are to outline seven types of qualitative Data Analysis techniques, to present step-by-step guidance for conducting these analyses via a computer-assisted qualitative Data Analysis software program (i.e., NVivo9), and to present screenshots of the Data Analysis process. Specifically, the following seven analyses are presented: constant comparison Analysis, classical content Analysis, keyword-in-context, word count, domain Analysis, taxonomic Analysis, and componential Analysis. It is our hope that providing a clear step-by-step process for conducting these analyses with NVivo9 will assist school psychology researchers in increasing the rigor of their qualitative Data Analysis procedures.
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an array of qualitative Data Analysis tools a call for Data Analysis triangulation
School Psychology Quarterly, 2007Co-Authors: Nancy L. Leech, Anthony J. OnwuegbuzieAbstract:One of the most important steps in the qualitative research process is Analysis of Data. The purpose of this article is to provide elements for understanding multiple types of qualitative Data Analysis techniques available and the importance of utilizing more than one type of Analysis, thus utilizing Data Analysis triangulation, in order to understand phenomenon more fully for school psychology research and beyond. The authors describe seven qualitative Analysis tools: methods of constant comparison, keywords-in-context, word count, classical content Analysis, domain Analysis, taxonomic Analysis, and componential Analysis. Then, the authors outline when to use each type of Analysis. In so doing, the authors use real qualitative Data to help distinguish the various types of analyses. Furthermore, flowcharts and tables are provided to help delineate when to choose each type of Analysis. Finally, the role of computer-assisted software in the qualitative Data-analytic process is discussed. As such, use of the analyses outlined in this article should help to promote rigor in qualitative research.
Zhanyu Ma - One of the best experts on this subject based on the ideXlab platform.
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The role of Data Analysis in the development of intelligent energy networks
IEEE Network, 2017Co-Authors: Zhanyu Ma, Jiyang Xie, Qie Sun, Zhongwei Si, Hailong Li, Jianhua Zhang, Jun GuoAbstract: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.
Jiyang Xie - One of the best experts on this subject based on the ideXlab platform.
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the role of Data Analysis in the development of intelligent energy networks
arXiv: Other Computer Science, 2017Co-Authors: Jiyang Xie, Qie Sun, Jianhua Zhang, Jun GuoAbstract: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.
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The role of Data Analysis in the development of intelligent energy networks
IEEE Network, 2017Co-Authors: Zhanyu Ma, Jiyang Xie, Qie Sun, Zhongwei Si, Hailong Li, Jianhua Zhang, Jun GuoAbstract: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.
Joshua M. Tebbs - One of the best experts on this subject based on the ideXlab platform.
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An Introduction to Categorical Data Analysis
Journal of the American Statistical Association, 2008Co-Authors: Christopher R. Bilder, Joshua M. TebbsAbstract:Bilder and Tebbs review An Introduction to Categorical Data Analysis (2nd ed.) by Alan Agresti.
