• Tuotekuvaus

  This book is a comprehensive scientific and practical guide to the digital transformation of industrial power systems. This book explores how artificial intelligence, real-time monitoring, and predictive analytics are reshaping the management of electricity in energy-intensive industries with continuous production cycles—such as metallurgy, chemical processing, and cement manufacturing. The core motivation behind this book lies in a global industrial challenge: how to ensure energy efficiency, reliability, and operational stability while maintaining uninterrupted technological processes. Traditional methods of load control and energy optimization are no longer sufficient in an era defined by Industry 4.0, Smart Grid technologies, IoT integration, and cyber-physical systems (CPS). This book bridges classical electrical-engineering theory with the latest advances in AI-driven forecasting, SCADA/EMS/BEMS systems, digital twins, and machine-learning-based optimization, offering readers both theoretical depth and actionable engineering tools. Across six logically connected chapters, this book guides readers from the fundamental laws of power consumption and system reliability to advanced topics such as adaptive-predictive architectures, stochastic modeling of industrial loads, and multi-agent control for real-time decision-making. Mathematical models, algorithmic frameworks, and applied case studies demonstrate how enterprises can reduce losses, manage reactive power, and forecast demand with higher precision. Each section combines analytical rigor with implementation strategies that can be directly applied in industrial environments. This book’s novel contribution lies in its interdisciplinary integration of electrical engineering, system theory, data science, and artificial intelligence into one unified methodological framework. It introduces an integral indicator of energy efficiency tailored for continuous production and proposes a hybrid AI-based control architecture capable of self-learning and adaptation under uncertainty. Written for a broad professional audience, this book benefits energy engineers, researchers, graduate students, and decision-makers involved in power supply, automation, and digital-energy management. Policymakers and sustainability strategists find it valuable for understanding how intelligent control systems can support national goals in green energy transition, industrial modernization, and carbon-reduction strategies. By combining theoretical insight with real-world relevance, this book serves as both a reference manual and a visionary roadmap for designing the next generation of intelligent, stable, and energy-efficient industrial power systems—a critical step toward the sustainable factories of the future.

• Tuotetiedot

  • Kustantaja / Valmistaja Springer Nature Switzerland AG
  • ISBN 9783032282408
  • Tuotekoodi 9783032282408
  • Alanimike Theoretical Foundations, Mathematical Models, and Artificial Intelligence Methods
  • Kirjoittajat Dinora A. Jalilova; Ikromjon U. Rakhmonov; Mahzuna F. Korjobova; Numon N. Niyozov; Nurbek N. Kurbonov; Vasily Ya. Ushakov
  • Sarja Power Systems
  • Kieli Englanti
  • Thema-luokitus Energia, energiantuotanto, energianjakelu ja energian varastointi
  • Ilmestymispäivä 04.09.2026
  • Vuosi 2026
  • Tuoteryhmä Ulk tiede, luonto
  • Tuotepääryhmä Ulkomaiset tietokirjat
  • Tuotelinja Kirjat
  • Sivumäärä 208
  • Pituus (mm) 235
  • Leveys (mm) 155
  • Tuotemuoto Kovakantinen
  • Tuoteturvallisuusyhteystiedot Springer Nature Customer Service Center GmbH, ProductSafety@springernature.com, Europaplatz 3,69115 Heidelberg, Germany, 69115, Heidelberg
  • ALV 13,5%

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