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Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Best Practices, Tools, and Case Studies

by Patrick Bangert

  • Publisher : Elsevier
  • Release : 2021-01-25
  • Pages : 274
  • ISBN : 0128226005
  • Language : En, Es, Fr & De
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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 set up 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 the 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 Accompanied by numerous supporting real-world case studies, providing practical evidence of both 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
Best Practices, Tools, and Case Studies

by Patrick Bangert

  • Publisher : Gulf Professional Publishing
  • Release : 2021-03-04
  • Pages : 306
  • ISBN : 0128209143
  • Language : En, Es, Fr & De
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Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

IoT Machine Learning Applications in Telecom, Energy, and Agriculture

IoT Machine Learning Applications in Telecom, Energy, and Agriculture
With Raspberry Pi and Arduino Using Python

by Puneet Mathur

  • Publisher : Apress
  • Release : 2020-05-09
  • Pages : 278
  • ISBN : 1484255496
  • Language : En, Es, Fr & De
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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.

Big Data Application in Power Systems

Big Data Application in Power Systems
A Book

by Reza Arghandeh,Yuxun Zhou

  • Publisher : Elsevier
  • Release : 2017-11-27
  • Pages : 480
  • ISBN : 0128119691
  • Language : En, Es, Fr & De
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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

Data Analytics in the Era of the Industrial Internet of Things

Data Analytics in the Era of the Industrial Internet of Things
A Book

by Aldo Dagnino

  • Publisher : Springer
  • Release : 2021-02-16
  • Pages : 129
  • ISBN : 9783030631383
  • Language : En, Es, Fr & De
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This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

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

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)
Emerging Trends and Challenges in Technology

by Yuzo Iano,Rangel Arthur,Osamu Saotome,Vânia Vieira Estrela,Hermes José Loschi

  • Publisher : Springer
  • Release : 2019-05-28
  • Pages : 665
  • ISBN : 303016053X
  • Language : En, Es, Fr & De
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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.

Applying Data Science

Applying Data Science
How to Create Value with Artificial Intelligence

by Arthur K. Kordon

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030363759
  • Language : En, Es, Fr & De
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Industry 4.0, AI, and Data Science

Industry 4.0, AI, and Data Science
Research Trends and Challenges

by Vikram Bali,Kakoli Banerjee,Narendra Kumar,Sanjay Gour,Sunil Kumar Chawla

  • Publisher : CRC Press
  • Release : 2021-07-21
  • Pages : 282
  • ISBN : 1000413454
  • Language : En, Es, Fr & De
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The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide.

Open Data and Energy Analytics

Open Data and Energy Analytics
A Book

by Benedetto Nastasi,Massimiliano Manfren,Michel Noussan

  • Publisher : MDPI
  • Release : 2020-06-25
  • Pages : 218
  • ISBN : 3039362186
  • Language : En, Es, Fr & De
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Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

Data Science for Wind Energy

Data Science for Wind Energy
A Book

by Yu Ding

  • Publisher : CRC Press
  • Release : 2019-06-04
  • Pages : 400
  • ISBN : 0429956517
  • Language : En, Es, Fr & De
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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

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
SPIoT-2020, Volume 1

by John MacIntyre

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030627438
  • Language : En, Es, Fr & De
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Building Enterprise IoT Applications

Building Enterprise IoT Applications
A Book

by Chandrasekar Vuppalapati

  • Publisher : CRC Press
  • Release : 2019-12-12
  • Pages : 442
  • ISBN : 0429508697
  • Language : En, Es, Fr & De
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McKinsey Global Institute predicts Internet of Things (IoT) could generate up to $11.1 trillion a year in economic value by 2025. Gartner Research Company expects 20 billion inter-connected devices by 2020 and, as per Gartner, the IoT will have a significant impact on the economy by transforming many enterprises into digital businesses and facilitating new business models, improving efficiency and increasing employee and customer engagement. It’s clear from above and our research that the IoT is a game changer and will have huge positive impact in foreseeable future. In order to harvest the benefits of IoT revolution, the traditional software development paradigms must be fully upgraded. The mission of our book, is to prepare current and future software engineering teams with the skills and tools to fully utilize IoT capabilities. The book introduces essential IoT concepts from the perspectives of full-scale software development with the emphasis on creating niche blue ocean products. It also: Outlines a fundamental full stack architecture for IoT Describes various development technologies in each IoT layer Explains IoT solution development from Product management perspective Extensively covers security and applicable threat models as part of IoT stack The book provides details of several IoT reference architectures with emphasis on data integration, edge analytics, cluster architectures and closed loop responses.

Profit Maximization Techniques for Operating Chemical Plants

Profit Maximization Techniques for Operating Chemical Plants
A Book

by Sandip K. Lahiri

  • Publisher : John Wiley & Sons
  • Release : 2020-05-01
  • Pages : 416
  • ISBN : 1119532175
  • Language : En, Es, Fr & De
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A systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins. The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvements—there is a plethora of information to help keep plant operations on budget. The book also includes information on: ● Take away methods and techniques for identifying and exploiting potential areas to improve profit within the plant ● Focus on latest Artificial Intelligence based modeling, knowledge discovery and optimization strategies to maximize profit in running plant. ● Describes procedure to develop advance process monitoring and fault diagnosis in running plant ● Thoughts on engineering design , best practices and monitoring to sustain profit improvements ● Step-by-step guides to identifying, building, and deploying improvement applications For leaders and technologists in the industry who want to maximize profit margins, this text provides basic concepts, guidelines, and step-by-step guides specifically for the chemical plant sector.

Handbook of Research on Smart Technology Models for Business and Industry

Handbook of Research on Smart Technology Models for Business and Industry
A Book

by Thomas, J. Joshua,Fiore, Ugo,Lechuga, Gilberto Perez,Kharchenko, Valeriy,Vasant, Pandian

  • Publisher : IGI Global
  • Release : 2020-06-19
  • Pages : 491
  • ISBN : 1799836460
  • Language : En, Es, Fr & De
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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.

Recent Trends in Mechatronics Towards Industry 4.0

Recent Trends in Mechatronics Towards Industry 4.0
Selected Articles from iM3F 2020, Malaysia

by Ahmad Fakhri Ab. Nasir

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 9813345977
  • Language : En, Es, Fr & De
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Data Science and Analytics

Data Science and Analytics
5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I

by Usha Batra,Nihar Ranjan Roy,Brajendra Panda

  • Publisher : Springer Nature
  • Release : 2020-05-27
  • Pages : 435
  • ISBN : 9811558272
  • Language : En, Es, Fr & De
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This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.

Smart Meter Data Analytics

Smart Meter Data Analytics
Electricity Consumer Behavior Modeling, Aggregation, and Forecasting

by Yi Wang,Qixin Chen,Chongqing Kang

  • Publisher : Springer Nature
  • Release : 2020-02-24
  • Pages : 293
  • ISBN : 9811526249
  • Language : En, Es, Fr & De
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This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track
European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V

by Yuxiao Dong

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030676706
  • Language : En, Es, Fr & De
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The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry
A Book

by Stephanie K. Ashenden

  • Publisher : Academic Press
  • Release : 2021-05-07
  • Pages : 264
  • ISBN : 0128204494
  • Language : En, Es, Fr & De
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The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

Predictive Analytics

Predictive Analytics
Data Mining, Machine Learning and Data Science for Practitioners

by Dursun Delen

  • Publisher : FT Press Analytics
  • Release : 2020-10-30
  • Pages : 350
  • ISBN : 9780136738510
  • Language : En, Es, Fr & De
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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.