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 book bridges 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.
