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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.
List of contents
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: Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights
Chapter 5: Data-Driven Tariff Design for Equitable Energy Distribution
Chapter 6: Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems
Chapter 7: Machine Learning Based Solutions for Renewable Energy Integration: Applications, Optimization and Grid Stability
Chapter 8: Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting
Chapter 9: Power System Resilience Evaluation: Data Challenge and Solutions
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
About the author
Hamidreza Arasteh is an Assistant Professor in the Power Systems Operation and Planning Research Department at the Niroo Research Institute, Tehran, Iran, and a Research Assistant at the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, Zhejiang, China. He specializes in energy management, smart grids, microgrids, and electricity markets, with numerous research contributions in energy management and the integration of data analytics into power system operations.
Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. A Senior Member of IEEE, his research focuses on demand response, distributed energy resources, and power system planning. He serves on editorial boards for several prestigious journals in the field.
Niki Moslemi is Head of the Power Systems Operation and Planning Research Department at the Niroo Research Institute in Tehran, Iran. She brings decades of experience in power quality, load forecasting, system resiliency, and data-driven energy strategies. Her leadership and research span multiple high-impact projects within the energy sector.
Josep M. Guerrero is with Zhejiang University, Hangzhou, Zhejiang, China, a Director of the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, China, and a Distinguished Senior Researcher at the Department of Electrical Engineering, University of Valladolid, Spain. His research interests include various aspects of microgrids, including power electronics and distributed energy resources.
