December 1, 2020

Download Ebook Free Machine Learning And Data Science In The Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Author : Patrick Bangert
Publisher : Elsevier
Release Date : 2021-03-15
Category : Technology & Engineering
Total pages :316
GET BOOK

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study-driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and setup ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Includes numerous supporting real-world case studies, providing practical guidance on best practices and potential pitfalls

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Release Date : 2021-03-01
Category : Computers
Total pages :300
GET BOOK

Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies explains critical facets around machine learning that are specifically tailored to oil and gas. Practical in its approach, this reference provides a chapter devoted to the early career engineer that is just starting in the industry. It then builds to a full-scale project that is supported by real-world case studies from various industry and academic contributors. Lessons learned and technology drivers are also discussed, creating a path for future engineers to apply. Rounding out with a glossary, this book delivers a reference that cuts through the hype to help today's petroleum engineers understand machine learning and where it can benefit their operations. Helps readers gain a practical understanding of machine learning used in oil and gas operations Presents change management skills that will help readers gain confidence in pursuing new technology Provides the workflow of a full-scale project and where machine learning is and isn't impactful

Big Data Application in Power Systems

Big Data Application in Power Systems
Author : Reza Arghandeh,Yuxun Zhou
Publisher : Elsevier
Release Date : 2017-11-27
Category : Science
Total pages :480
GET BOOK

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

Applying Data Science

Applying Data Science
Author : Arthur K. Kordon
Publisher : Springer Nature
Release Date : 2020
Category :
Total pages :129
GET BOOK

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)
Author : Yuzo Iano,Rangel Arthur,Osamu Saotome,Vânia Vieira Estrela,Hermes José Loschi
Publisher : Springer
Release Date : 2019-05-28
Category : Technology & Engineering
Total pages :665
GET BOOK

This book presents the Proceedings of The 4th Brazilian Technology Symposium (BTSym'18). Part I of the book discusses current technological issues on Systems Engineering, Mathematics and Physical Sciences, such as the Transmission Line, Protein-modified mortars, Electromagnetic Properties, Clock Domains, Chebyshev Polynomials, Satellite Control Systems, Hough Transform, Watershed Transform, Blood Smear Images, Toxoplasma Gondi, Operation System Developments, MIMO Systems, Geothermal-Photovoltaic Energy Systems, Mineral Flotation Application, CMOS Techniques, Frameworks Developments, Physiological Parameters Applications, Brain Computer Interface, Artificial Neural Networks, Computational Vision, Security Applications, FPGA Applications, IoT, Residential Automation, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Digital Image Processing, Patters Recognition, Machine Learning, Photocatalytic Process, Physical-chemical analysis, Smoothing Filters, Frequency Synthesizers, Voltage Controlled Ring Oscillator, Difference Amplifier, Photocatalysis and Photodegradation. Part II of the book discusses current technological issues on Human, Smart and Sustainable Future of Cities, such as the Digital Transformation, Data Science, Hydrothermal Dispatch, Project Knowledge Transfer, Immunization Programs, Efficiency and Predictive Methods, PMBOK Applications, Logistics Process, IoT, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Fingerspelling Recognition, Cognitive Ergonomics, Ecosystem services, Environmental, Ecosystem services valuation, Solid Waste and University Extension. BTSym is the brainchild of Prof. Dr. Yuzo Iano, who is responsible for the Laboratory of Visual Communications (LCV) at the Department of Communications (DECOM) of the Faculty of Electrical and Computing Engineering (FEEC), State University of Campinas (UNICAMP), Brazil.

IoT Machine Learning Applications in Telecom, Energy, and Agriculture

IoT Machine Learning Applications in Telecom, Energy, and Agriculture
Author : Puneet Mathur
Publisher : Apress
Release Date : 2020-05-09
Category : Computers
Total pages :278
GET BOOK

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.

Data Science for Wind Energy

Data Science for Wind Energy
Author : Yu Ding
Publisher : CRC Press
Release Date : 2019-06-04
Category : Business & Economics
Total pages :400
GET BOOK

Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights

Data Analytics for Renewable Energy Integration

Data Analytics for Renewable Energy Integration
Author : Wei Lee Woon,Zeyar Aung,Stuart Madnick
Publisher : Springer
Release Date : 2014-11-20
Category : Computers
Total pages :151
GET BOOK

This book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.

Handbook of Research on Smart Technology Models for Business and Industry

Handbook of Research on Smart Technology Models for Business and Industry
Author : Thomas, J. Joshua,Fiore, Ugo,Lechuga, Gilberto Perez,Kharchenko, Valeriy,Vasant, Pandian
Publisher : IGI Global
Release Date : 2020-06-19
Category : Technology & Engineering
Total pages :491
GET BOOK

Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

New Horizons for a Data-Driven Economy

New Horizons for a Data-Driven Economy
Author : José María Cavanillas,Edward Curry,Wolfgang Wahlster
Publisher : Springer
Release Date : 2016-04-04
Category : Computers
Total pages :303
GET BOOK

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publisher : IGI Global
Release Date : 2019-11-29
Category : Computers
Total pages :355
GET BOOK

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.

Big Data, Big Analytics

Big Data, Big Analytics
Author : Michael Minelli,Michele Chambers,Ambiga Dhiraj
Publisher : John Wiley & Sons
Release Date : 2012-12-27
Category : Business & Economics
Total pages :224
GET BOOK

Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making
Author : Cengiz Kahraman,Selcuk Cebi,Sezi Cevik Onar,Basar Oztaysi,A. Cagri Tolga,Irem Ucal Sari
Publisher : Springer
Release Date : 2019-07-05
Category : Computers
Total pages :1392
GET BOOK

This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.

Predictive Analytics

Predictive Analytics
Author : Dursun Delen
Publisher : FT Press Analytics
Release Date : 2020-10-30
Category : Business & Economics
Total pages :350
GET BOOK

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.

Data Science and Intelligent Applications

Data Science and Intelligent Applications
Author : Ketan Kotecha,Vincenzo Piuri,Hetalkumar N. Shah,Rajan Patel
Publisher : Springer Nature
Release Date : 2020-06-17
Category : Technology & Engineering
Total pages :576
GET BOOK

This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.