Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Author: Patrick Bangert
Publsiher: Elsevier
Total Pages: 274
Release: 2021-01-29
ISBN 10: 0128197420
ISBN 13: 9780128197424
Language: EN, FR, DE, ES & NL

Machine Learning and Data Science in the Power Generation Industry Book Review:

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 Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Author: Patrick Bangert
Publsiher: Elsevier
Total Pages: 274
Release: 2021-01-14
ISBN 10: 0128226005
ISBN 13: 9780128226001
Language: EN, FR, DE, ES & NL

Machine Learning and Data Science in the Power Generation Industry Book Review:

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
Author: Patrick Bangert
Publsiher: Gulf Professional Publishing
Total Pages: 306
Release: 2021-03-04
ISBN 10: 0128209143
ISBN 13: 9780128209141
Language: EN, FR, DE, ES & NL

Machine Learning and Data Science in the Oil and Gas Industry Book Review:

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)

Advances in Information and Communication

Advances in Information and Communication
Author: Kohei Arai
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 303098012X
ISBN 13: 9783030980122
Language: EN, FR, DE, ES & NL

Advances in Information and Communication Book Review:

Advances of Machine Learning in Clean Energy and the Transportation Industry

Advances of Machine Learning in Clean Energy and the Transportation Industry
Author: Pandian Vasant
Publsiher: Unknown
Total Pages: 135
Release: 2021-11-30
ISBN 10: 9781685072117
ISBN 13: 1685072119
Language: EN, FR, DE, ES & NL

Advances of Machine Learning in Clean Energy and the Transportation Industry Book Review:

This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe.Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data.The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.

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
Author: Stephanie K. Ashenden
Publsiher: Academic Press
Total Pages: 264
Release: 2021-04-23
ISBN 10: 0128204494
ISBN 13: 9780128204498
Language: EN, FR, DE, ES & NL

The Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical Industry Book Review:

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

Artificial Intelligence Machine Learning and Data Science Technologies

Artificial Intelligence  Machine Learning  and Data Science Technologies
Author: Neeraj Mohan,Ruchi Singla,Priyanka Kaushal,Seifedine Kadry
Publsiher: CRC Press
Total Pages: 310
Release: 2021-10-12
ISBN 10: 1000460541
ISBN 13: 9781000460544
Language: EN, FR, DE, ES & NL

Artificial Intelligence Machine Learning and Data Science Technologies Book Review:

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

Artificial Intelligence for Renewable Energy systems

Artificial Intelligence for Renewable Energy systems
Author: Ashutosh Kumar Dubey,Sushil Narang,Arun Lal Srivastav,Abhishek Kumar,Vicente García-Díaz
Publsiher: Woodhead Publishing
Total Pages: 500
Release: 2022-08-15
ISBN 10: 9780323903967
ISBN 13: 0323903967
Language: EN, FR, DE, ES & NL

Artificial Intelligence for Renewable Energy systems Book Review:

Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention. Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms. Includes real-time applications that illustrates artificial intelligence and machine learning for various renewable systems Features a templated approach that can be used to explore results, with scientific implications followed by detailed case studies Covers computational capabilities and varieties for renewable system design

Strategic Approaches to Energy Management

Strategic Approaches to Energy Management
Author: Serhat Yüksel,Hasan Dinçer
Publsiher: Springer Nature
Total Pages: 299
Release: 2021-08-23
ISBN 10: 3030767833
ISBN 13: 9783030767839
Language: EN, FR, DE, ES & NL

Strategic Approaches to Energy Management Book Review:

This book introduces current managerial approaches to energy production and energy use. The volume analyses how to manage technological developments that contribute to lowering the price of energy production and also focuses on the impact renewable energy sources that provide continuity in energy production and how to manage it. The book presents studies on the effectiveness of wind, solar, biomass, geothermal and hydroelectric energies and discusses current technological approaches to prevent environmental pollution such as carbon capture and storage. Furthermore, the book includes sustainable economic and financial strategies to use energy more effectively and efficiently. It thus appeals not only to an academic readership but also to energy management professionals working in this field.

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
Publsiher: Springer
Total Pages: 665
Release: 2019-05-28
ISBN 10: 303016053X
ISBN 13: 9783030160531
Language: EN, FR, DE, ES & NL

Proceedings of the 4th Brazilian Technology Symposium BTSym 18 Book Review:

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
Author: Arthur K. Kordon
Publsiher: Springer Nature
Total Pages: 494
Release: 2020-09-12
ISBN 10: 3030363759
ISBN 13: 9783030363758
Language: EN, FR, DE, ES & NL

Applying Data Science Book Review:

This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.

Digital Transformation in Industry

Digital Transformation in Industry
Author: Vikas Kumar
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 3030946177
ISBN 13: 9783030946173
Language: EN, FR, DE, ES & NL

Digital Transformation in Industry Book Review:

Industry 4 0 AI and Data Science

Industry 4 0  AI  and Data Science
Author: Vikram Bali,Kakoli Banerjee,Narendra Kumar,Sanjay Gour,Sunil Kumar Chawla
Publsiher: CRC Press
Total Pages: 282
Release: 2021-07-21
ISBN 10: 1000413454
ISBN 13: 9781000413458
Language: EN, FR, DE, ES & NL

Industry 4 0 AI and Data Science Book Review:

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.

Smart Sensor Networks Using AI for Industry 4 0

Smart Sensor Networks Using AI for Industry 4 0
Author: Soumya Ranjan Nayak,Biswa Mohan Sahoo,Muthukumaran Malarvel,Jibitesh Mishra
Publsiher: CRC Press
Total Pages: 262
Release: 2021-10-10
ISBN 10: 1000458512
ISBN 13: 9781000458510
Language: EN, FR, DE, ES & NL

Smart Sensor Networks Using AI for Industry 4 0 Book Review:

Smart Sensor Networks (WSNs) using AI have left a mark on the lives of all by aiding in various sectors, such as manufacturing, education, healthcare, and monitoring of the environment and industries. This book covers recent AI applications and explores aspects of modern sensor technologies and the systems needed to operate them. The book reviews the fundamental concepts of gathering, processing, and analyzing different AI-based models and methods. It covers recent WSN techniques for the purpose of effective network management on par with the standards laid out by international organizations in related fields and focuses on both core concepts along with major applicational areas. The book will be used by technical developers, academicians, data sciences, industrial professionals, researchers, and students interested in the latest innovations on problem-oriented processing techniques in sensor networks using IoT and evolutionary computer applications for Industry 4.0.

Data Science for Wind Energy

Data Science for Wind Energy
Author: Yu Ding
Publsiher: CRC Press
Total Pages: 400
Release: 2019-06-04
ISBN 10: 0429956509
ISBN 13: 9780429956508
Language: EN, FR, DE, ES & NL

Data Science for Wind Energy Book Review:

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. 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

Application of Machine Learning and Deep Learning Methods to Power System Problems

Application of Machine Learning and Deep Learning Methods to Power System Problems
Author: Morteza Nazari-Heris,Somayeh Asadi,Behnam Mohammadi-Ivatloo,Moloud Abdar,Houtan Jebelli,Milad Sadat-Mohammadi
Publsiher: Springer Nature
Total Pages: 391
Release: 2021-11-21
ISBN 10: 3030776964
ISBN 13: 9783030776961
Language: EN, FR, DE, ES & NL

Application of Machine Learning and Deep Learning Methods to Power System Problems Book Review:

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

Data Science and Computational Intelligence

Data Science and Computational Intelligence
Author: K. R. Venugopal
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 3030912442
ISBN 13: 9783030912444
Language: EN, FR, DE, ES & NL

Data Science and Computational Intelligence Book Review:

Machine Learning and Knowledge Discovery in Databases Applied Data Science Track

Machine Learning and Knowledge Discovery in Databases  Applied Data Science Track
Author: Yuxiao Dong,Nicolas Kourtellis,Barbara Hammer,Jose A. Lozano
Publsiher: Springer Nature
Total Pages: 554
Release: 2021-09-09
ISBN 10: 3030865142
ISBN 13: 9783030865146
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Applied Data Science Track Book Review:

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Application of Big Data in Petroleum Streams

Application of Big Data in Petroleum Streams
Author: Jay Gohil,Manan Shah
Publsiher: CRC Press
Total Pages: 182
Release: 2022-05-09
ISBN 10: 1000580024
ISBN 13: 9781000580020
Language: EN, FR, DE, ES & NL

Application of Big Data in Petroleum Streams Book Review:

The book aims to provide comprehensive knowledge and information pertaining to application or implementation of big data in the petroleum industry and its operations (such as exploration, production, refining and finance). The book covers intricate aspects of big data such as 6Vs, benefits, applications, implementation, research work and real-world implementation pertaining to each petroleum-associated operation in a concise manner that aids the reader to apprehend the overview of big data’s role in the industry. The book resonates with readers who wish to understand the intricate details of working with big data (along with data science, machine learning and artificial intelligence) in general and how it affects and impacts an entire industry. As the book builds various concepts of big data from scratch to industry level, readers who wish to gain big data-associated knowledge of industry level in simple language from the very fundamentals would find this a wonderful read.

Machine Learning Optimization and Data Science

Machine Learning  Optimization  and Data Science
Author: Giuseppe Nicosia
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
ISBN 10: 3030954676
ISBN 13: 9783030954673
Language: EN, FR, DE, ES & NL

Machine Learning Optimization and Data Science Book Review: