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Könyv Big Data Application in Power Systems Reza Arghandeh

Big Data Application in Power Systems

Szerző: Reza Arghandeh, Yuxun Zhou
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Elsevier Science
Elérhetőség: Kiadói készleten rendelésre
Küldés 28-34 napon belül
53 571 Ft
Big Data Application in Power Systems, Second Edition presents a thorough update of the previous vol...

Információk a könyvről

Szerző
Reza Arghandeh, Yuxun Zhou
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2024
oldal
500
EAN
9780443215247
ISBN
0443215243
Enbook ID
44517582
Kiadó
Elsevier Science
Súly
720
Méretek
151 x 229

Teljes leírás

Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. Provides a total refresh to include the most up-to-date research, developments, and challengesFocuses on practical techniques including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal dataEngages with cross-disciplinary lessons drawing on the impact of intersectional technology including statistics, computer science, and bioinformaticsIncludes 5 brand new chapters on hot topics, ranging from uncertainty decision making to features selection methods and the opportunities provided by social network data

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