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Big Data Application in Power Systems

Big Data Application in Power Systems Book
Author : Reza Arghandeh,Yuxun Zhou
Publisher : Elsevier
Release : 2017-11-27
ISBN : 0128119691
Language : En, Es, Fr & De

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Book Description :

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Innovation in Energy Systems

Innovation in Energy Systems Book
Author : Taha Selim Ustun
Publisher : BoD – Books on Demand
Release : 2019-11-27
ISBN : 1789841070
Language : En, Es, Fr & De

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Book Description :

It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field.

Big Data Analytics in Future Power Systems

Big Data Analytics in Future Power Systems Book
Author : Ahmed F. Zobaa,Trevor J. Bihl
Publisher : CRC Press
Release : 2018-08-14
ISBN : 1351601288
Language : En, Es, Fr & De

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Book Description :

Power systems are increasingly collecting large amounts of data due to the expansion of the Internet of Things into power grids. In a smart grids scenario, a huge number of intelligent devices will be connected with almost no human intervention characterizing a machine-to-machine scenario, which is one of the pillars of the Internet of Things. The book characterizes and evaluates how the emerging growth of data in communications networks applied to smart grids will impact the grid efficiency and reliability. Additionally, this book discusses the various security concerns that become manifest with Big Data and expanded communications in power grids. Provide a general description and definition of big data, which has been gaining significant attention in the research community. Introduces a comprehensive overview of big data optimization methods in power system. Reviews the communication devices used in critical infrastructure, especially power systems; security methods available to vet the identity of devices; and general security threats in CI networks. Presents applications in power systems, such as power flow and protection. Reviews electricity theft concerns and the wide variety of data-driven techniques and applications developed for electricity theft detection.

Handbook of Research on Big Data Storage and Visualization Techniques

Handbook of Research on Big Data Storage and Visualization Techniques Book
Author : Segall, Richard S.,Cook, Jeffrey S.
Publisher : IGI Global
Release : 2018-01-05
ISBN : 1522531432
Language : En, Es, Fr & De

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Book Description :

The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Design and Optimization of Biogas Energy Systems

Design and Optimization of Biogas Energy Systems Book
Author : Prashant Baredar,Vikas Khare,Savita Nema
Publisher : Academic Press
Release : 2020-06-18
ISBN : 0128227192
Language : En, Es, Fr & De

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Book Description :

Design and Optimization of Biogas Energy Systems presents an overview on planning, implementing, assessing and optimizing biogas systems, from fuel conversion to power generation. The book introduces the fundamental elements of bioenergy systems, highlighting the specificities of biogas systems. It discusses the current state of their adoption at a global level and the challenges faced by designers and operators. Methods for sizing, simulating and modeling are discussed, including prefeasibility analysis, available production processes, integration into hybrid energy systems, and the application of Big Data analysis and game theory concepts. All chapters include real-life examples and exercises to illustrate the topics being covered. The book goes beyond theory to offer practical knowledge of methods to reach solutions to key challenges in the field. This is a valuable resource for researchers, practitioners and graduate students interested in developing smart, reliable and sustainable biogas technologies. Provides an applied approach to biogas systems, from technology fundamentals, to economic and environmental assessment Explores control methods and reliability prediction of each system component, including modeling and simulation with HOMER and MATLAB Discusses the use of Big Data analysis, numerical methods, and Game Theory for plant assessment

Big Data in Engineering Applications

Big Data in Engineering Applications Book
Author : Sanjiban Sekhar Roy,Pijush Samui,Ravinesh Deo,Stavros Ntalampiras
Publisher : Springer
Release : 2018-05-02
ISBN : 9811084769
Language : En, Es, Fr & De

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Book Description :

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

AI and Big Data on IBM Power Systems Servers

AI and Big Data on IBM Power Systems Servers Book
Author : Scott Vetter,Ivaylo B. Bozhinov,Anto A John,Rafael Freitas de Lima,Ahmed.(Mash) Mashhour,James Van Oosten,Fernando Vermelho,Allison White,IBM Redbooks
Publisher : IBM Redbooks
Release : 2019-04-10
ISBN : 0738457515
Language : En, Es, Fr & De

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Book Description :

As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Big Data Analytics Systems Algorithms Applications

Big Data Analytics  Systems  Algorithms  Applications Book
Author : C.S.R. Prabhu,Aneesh Sreevallabh Chivukula,Aditya Mogadala,Rohit Ghosh,L.M. Jenila Livingston
Publisher : Springer Nature
Release : 2019-10-14
ISBN : 9811500940
Language : En, Es, Fr & De

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Book Description :

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.

Data Mining Application to Power Grid PMU Data

Data Mining Application to Power Grid PMU Data Book
Author : Tianzhixi Yin
Publisher :
Release : 2018
ISBN : 9780438020108
Language : En, Es, Fr & De

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Book Description :

The reliability of the power system is of vital importance to daily life. Power outages, either small or big, cause economic loss and inconvenience. The effort to better understand the behaviors of the power grid has a long history. Several years ago, engineers and researchers started to modernize the national power grid in the United States by moving it into the next generation called SmartGrid. The massive installation of Phasor Measurement Units (PMUs) is the highlight of this movement. Compared to the sensors from the last generation in the power grid, PMUs can produce more accurate and rapid information regarding the state of the grid. Data from PMUs are usually 30 samples to 60 samples per second and contain both voltage magnitude and angle measurements. The data is high-dimensional with hundreds or thousands of signals and is highly correlated across signals. One advantage of PMU data is that it is time aligned throught GPS. The biggest challenge in the use of PMU data is the massive amount of data, which causes difficulties for storage, pre-processing, analysis, and visualization. A whole year of PMU data can be several terabytes depending on the number of signals in the data. In fact, the information provided by PMUs is now so big that it is difficult for scientists to handle or easily comprehend. Meanwhile, many exciting accomplishments have been seen in various fields using data mining. Data mining has become increasingly important with the appearance of various kinds of big data. The power grid data analytics is a good example of such a big data problem. There has been an increase of data mining applications in the power systems research field in recent years, partly due to advancements in data mining. There has been much work on this topic in the last 10 years. This research work, which has been done at the University of Wyoming and Pacific Northwest National Laboratory, consists of data analytics on simulated PMU data from the MinniWECC system and real PMU data from the Western Electricity Coordinating Council (WECC) system for the 2008 to 2009 and 2016 to 2017 operating seasons. This work contains measurement-based power system offline studies involving event detection, event classification, abnormal operating conditions, and potential online applications. The simulated study discusses the implementation and performance of various machine learning algorithms for classifying power system event types and event locations. A simple feature extraction method is applied. The contribution of this study is to demonstrate how data mining techniques can be used to incorporate information from PMU data to assess the system condition. Moreover, data mining techniques are applied to historical data consisting of PMU measurements from WECC from June 2008 to June 2009. The main objective is to classify abnormal and normal power grid modal behavior of the WECC interconnect at the daily scale. The data is transformed to the frequency domain to represent operating conditions of each day. A closer investigation of misclassified days is also conducted to look at abnormal system behaviors at the hourly scale. The research contribution of this study is the application of data mining techniques to power grid data in the frequency domain to identify various power system events, especially large scale events both in size and in duration. The third part of this dissertation extends the findings in the simulated study and applies updated methodologies to PMU data from October 2016 to May 2017. This work involves training machine learning algorithms to detect and classify power system events in the time domain. Different machine learning algorithms are applied and a new algorithm is developed to enhance the final algorithm. The results show that the proposed algorithm can successfully detect and classify power system events at high accuracy in under one second. This research demonstrates the potential for an on-line application of achieving near real-time power system situational awareness.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book
Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publisher : IGI Global
Release : 2019-11-29
ISBN : 1799811948
Language : En, Es, Fr & De

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Book Description :

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

IBM Power System S822LC for Big Data Technical Overview and Introduction

IBM Power System S822LC for Big Data  Technical Overview and Introduction Book
Author : Scott Vetter,David Barron,Alexandre Bicas Caldeira,Volker Haug,IBM Redbooks
Publisher : IBM Redbooks
Release : 2017-08-29
ISBN : 0738455776
Language : En, Es, Fr & De

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Book Description :

This IBM® RedpaperTM publication is a comprehensive guide that covers the IBM Power System S822LC for Big Data (8001-22C) server that uses the latest IBM POWER8® processor technology and supports Linux operating systems (OSs). The objective of this paper is to introduce the Power S822LC for Big Data offerings and their relevant functions as related to targeted application workloads. The new Linux scale-out systems provide differentiated performance, scalability, and low acquisition cost, including: Consolidated server footprint with up to 66% more virtual machines (VMs) per server than competitive x86 servers Superior data throughput and performance for high-value Linux workloads, such as big data, analytic, and industry applications Up to 12 LFF drives that are installed within the chassis to meet storage-rich application requirements Superior application performance due to a 2x per core performance advantage over x86-based systems Leadership data through put enabled by POWER8 multithreading with up to 4x more threads than x86 designs Acceleration of bid data workloads with up to two GPUs and superior I/O bandwidth with Coherent Accelerator Processor Interface (CAPI) This publication is for professionals who want to acquire a better understanding of IBM Power SystemsTM products. The intended audience includes: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors

Big Data Revolution

Big Data Revolution Book
Author : Rob Thomas,Patrick McSharry
Publisher : John Wiley & Sons
Release : 2015-01-05
ISBN : 1118943724
Language : En, Es, Fr & De

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Book Description :

Exploit the power and potential of Big Data to revolutionizebusiness outcomes Big Data Revolution is a guide to improving performance,making better decisions, and transforming business through theeffective use of Big Data. In this collaborative work by an IBMVice President of Big Data Products and an Oxford Research Fellow,this book presents inside stories that demonstrate the power andpotential of Big Data within the business realm. Readers are guidedthrough tried-and-true methodologies for getting more out of data,and using it to the utmost advantage. This book describes the majortrends emerging in the field, the pitfalls and triumphs beingexperienced, and the many considerations surrounding Big Data, allwhile guiding readers toward better decision making from theperspective of a data scientist. Companies are generating data faster than ever before, andmanaging that data has become a major challenge. With the rightstrategy, Big Data can be a powerful tool for creating effectivebusiness solutions – but deep understanding is key whenapplying it to individual business needs. Big DataRevolution provides the insight executives need to incorporateBig Data into a better business strategy, improving outcomes withinnovation and efficient use of technology. Examine the major emerging patterns in Big Data Consider the debate surrounding the ethical use of data Recognize patterns and improve personal and organizationalperformance Make more informed decisions with quantifiable results In an information society, it is becoming increasingly importantto make sense of data in an economically viable way. It can drivenew revenue streams and give companies a competitive advantage,providing a way forward for businesses navigating an increasinglycomplex marketplace. Big Data Revolution provides expertinsight on the tool that can revolutionize industries.

Big Data Analytics Strategies for the Smart Grid

Big Data Analytics Strategies for the Smart Grid Book
Author : Carol L. Stimmel
Publisher : CRC Press
Release : 2016-04-19
ISBN : 1482218291
Language : En, Es, Fr & De

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Book Description :

By implementing a comprehensive data analytics program, utility companies can meet the continually evolving challenges of modern grids that are operationally efficient, while reconciling the demands of greenhouse gas legislation and establishing a meaningful return on investment from smart grid deployments.Readable and accessible, Big Data Analytic

Real Time Stability in Power Systems

Real Time Stability in Power Systems Book
Author : Savu C. Savulescu
Publisher : Springer
Release : 2014-07-10
ISBN : 3319066803
Language : En, Es, Fr & De

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Book Description :

This pioneering volume has been updated and enriched to reflect the state-of-the-art in blackout prediction and prevention. It documents and explains background and algorithmic aspects of the most successful steady-state, transient and voltage stability solutions available today in real-time. It also describes new, cutting-edge stability applications of synchrophasor technology, and captures industry acceptance of metrics and visualization tools that quantify and monitor the distance to instability. Expert contributors review a broad spectrum of additionally available techniques, such as trajectory sensitivities, ensuring this volume remains the definitive resource for industry practitioners and academic researchers in this critical area of power system operations.

Electronics Automation and Engineering of Power Systems

Electronics  Automation and Engineering of Power Systems Book
Author : Rong Qing Liang
Publisher : Trans Tech Publications Ltd
Release : 2015-02-25
ISBN : 3038268003
Language : En, Es, Fr & De

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Book Description :

Collection of selected, peer reviewed papers from the International Forum on Electrical Engineering and Automation & the 2014 International Conference on Lighting Technology and Electronic Engineering (ICLTEE 2014), November 29-30, 2014, Guangzhou, China. The 191 papers are grouped as follows: Chapter 1: Sensors, Measurements, Systems of Monitoring, Detection and Diagnostics; Chapter 2: Mechatronics, Robotics, Control and Automation; Chapter 3: Technologies of Intelligent Systems; Chapter 4: Practice of Data Processing for Intelligent Systems; Chapter 5: Power Systems Engineering; Chapter 6: Photovoltaic Power Systems; Chapter 7: Power Electronics and Circuits, Electrical Machines and Equipments; Chapter 8: Modern Technology of Lighting

Computational Methods for Large Sparse Power Systems Analysis

Computational Methods for Large Sparse Power Systems Analysis Book
Author : S. A. Soman,S. A. Khaparde,Shubha Pandit
Publisher : Springer Science & Business Media
Release : 2002
ISBN : 9780792375913
Language : En, Es, Fr & De

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Book Description :

Computational methods in Power Systems require significant inputs from diverse disciplines, such as data base structures, numerical analysis etc. Strategic decisions in sparsity exploitation and algorithm design influence large-scale simulation and high-speed computations. Selection of programming paradigm shapes the design, its modularity and reusability. This has a far reaching effect on software maintenance. Computational Methods for Large Sparse Power Systems Analysis: An Object Oriented Approach provides a unified object oriented (OO) treatment for power system analysis. Sparsity exploitation techniques in OO paradigm are emphasized to facilitate large scale and fast computing. Specific applications like large-scale load flow, short circuit analysis, state estimation and optimal power flow are discussed within this framework. A chapter on modeling and computational issues in power system dynamics is also included. Motivational examples and illustrations are included throughout the book. A library of C++ classes provided along with this book has classes for transmission lines, transformers, substation etc. A CD-ROM with C++ programs is also included. It contains load flow, short circuit analysis and network topology processor applications. Power system data is provided and systems up to 150 buses can be studied. Other Special Features: This book is the first of its kind, covering power system applications designed with an OO perspective. Chapters on object orientation for modeling of power system computations, data structure, large sparse linear system solver, sparse QR decomposition in an OO framework are special features of this book.

Power Generation Operation and Control

Power Generation  Operation  and Control Book
Author : Allen J. Wood,Bruce F. Wollenberg,Gerald B. Sheblé
Publisher : John Wiley & Sons
Release : 2013-12-18
ISBN : 1118733916
Language : En, Es, Fr & De

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Book Description :

A thoroughly revised new edition of the definitive work on power systems best practices In this eagerly awaited new edition, Power Generation, Operation, and Control continues to provide engineers and academics with a complete picture of the techniques used in modern power system operation. Long recognized as the standard reference in the field, the book has been thoroughly updated to reflect the enormous changes that have taken place in the electric power industry since the Second Edition was published seventeen years ago. With an emphasis on both the engineering and economic aspects of energy management, the Third Edition introduces central "terminal" characteristics for thermal and hydroelectric power generation systems, along with new optimization techniques for tackling real-world operating problems. Readers will find a range of algorithms and methods for performing integrated economic, network, and generating system analysis, as well as modern methods for power system analysis, operation, and control. Special features include: State-of-the-art topics such as market simulation, multiple market analysis, contract and market bidding, and other business topics Chapters on generation with limited energy supply, power flow control, power system security, and more An introduction to regulatory issues, renewable energy, and other evolving topics New worked examples and end-of-chapter problems A companion website with additional materials, including MATLAB programs and power system sample data sets

Predictive Modelling for Energy Management and Power Systems Engineering

Predictive Modelling for Energy Management and Power Systems Engineering Book
Author : Ravinesh Deo,Pijush Samui,Sanjiban Sekhar Roy
Publisher : Elsevier
Release : 2020-09-30
ISBN : 012817773X
Language : En, Es, Fr & De

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Book Description :

Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format

Big Data in Complex Systems

Big Data in Complex Systems Book
Author : Aboul Ella Hassanien,Ahmad Taher Azar,Vaclav Snasael,Janusz Kacprzyk,Jemal H. Abawajy
Publisher : Springer
Release : 2015-01-02
ISBN : 331911056X
Language : En, Es, Fr & De

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Book Description :

This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.