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DATENGETRIEBENES ENERGIEMANAGEMENT UND TARIFOPTIMIERUNG IN ENERGIEANLAGEN: GESTALTEN-
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Standort: Lansdale, PA, USA
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eBay-Artikelnr.:357414857043
Artikelmerkmale
- Artikelzustand
- ISBN
- 9781394290277
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1394290276
ISBN-13
9781394290277
eBay Product ID (ePID)
17078708956
Product Key Features
Number of Pages
288 Pages
Publication Name
Data-Driven Energy Management and Tariff Optimization in Power Systems : Shaping the Future of Electricity Distribution Through Analytics
Language
English
Publication Year
2025
Subject
Engineering (General), Data Modeling & Design, Energy, Power Resources / General
Type
Textbook
Subject Area
Computers, Technology & Engineering, Science
Format
Hardcover
Dimensions
Item Weight
23.5 Oz
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2025-025149
Table Of Content
Chapter 1: Fundamentals of Power System Data and Analytics Chapter 2: Advanced Predictive Modeling for Energy Consumption and Demand Chapter 3: Demand Response and Customer-Centric Energy Management Chapter 4: Power System Resilience Evaluation: Data Challenge and Solutions Chapter 5: Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights Chapter 6: Data-Driven Tariff Design for Equitable Energy Distribution Chapter 7: Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems Chapter 8: Machine Learning Based Solutions for Renewable Energy Integration: Applications, Optimization and Grid Stability Chapter 9: Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting Chapter 10: Non-intrusive Load Monitoring in Smart Grids using Deep Learning Approach Chapter 11: Data-Driven Approaches for Power System State Estimation Chapter 12: Power System Cyber-Physical Security and Resiliency based on Data-driven Methods Chapter 13: Application of Artificial Intelligence in Under Voltage Load Shedding in Digitalized Power Systems: an in-Depth Review
Synopsis
Presents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning. Drawing on global expertise and real-world case studies, the authors bridge the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book's accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges. Integrating rigorous analysis with application-oriented strategies, this book: Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff design Provides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrations Highlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy data Addresses the integration of renewable energy sources into existing infrastructures through data-driven optimization Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.
LC Classification Number
TK3091.D347 2025
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