AI and Digitalization in Energy Management

by Antonio Sanfilippo (Editor), Sertac Bayhan (Editor), Dragan Boscovic (Editor)

Overview

Energy management involves the planning and operation of energy production, consumption, distribution and storage, with objectives including resource conservation, climate protection and cost savings. Growth in renewable energy - essential for the transition to a decarbonised energy system - adds the challenge of intermittency, making energy management all the more important.

This book explores the role of digitalization and the growing interest in using AI for energy management. Edited by a team of senior scientists, with ample project and industry experience, the book systematically covers methods, applications including forecasting and maintenance, and economic aspects.

The chapters cover solar and meteorological data collection and simulation, digital twins and data wrangling, ML, game theory and AI for energy management, edge to cloud, federated learning and quantum computing for energy management, intra-hour solar forecasting, use of synchrophasor technology, AI-powered energy conversion and resilience, explainable AI, electric mobility integration, optimization for EV adoption, predictive PV maintenance, AI and robotics for PV inspection, and blockchain-based microgrids.

AI and Digitalization in Energy Management will prove a useful resource for researchers in universities, research institutes and in industry involved with clean energy and AI systems, grid operators, as well as energy policy makers and advanced students in energy engineering.

Product Details

ISBN-13:	9781839539794
Publisher:	Institution of Engineering & Technology
Publication date:	09/16/2025
Series:	Energy Engineering
Pages:	492
Product dimensions:	6.14(w) x 9.21(h) x 1.06(d)

About the Author

Antonio Sanfilippo is chief scientist at Qatar Environment and Energy Research Institute (QEERI), where he has led the energy management program and several projects funded by Qatar Research, Development and Innovation Council since 2014. While at QEERI, he has managed the establishment of several renewable energy and smart grid capabilities that have become national points of reference for local and international stakeholders, including a nationwide network of solar monitoring stations, a 100 kWp microgrid testbed and a network of Phasor Measurement Units in Education City, Doha, Qatar. Prior to QEERI, he was chief scientist at the Pacific Northwest National Laboratory in the USA, where he was awarded the Laboratory Director's Award for Exceptional Scientific Achievement. He has also held positions as research director in the private sector, senior consultant at the European Commission, and research supervisor and group manager at SHARP Laboratories of Europe. He holds MA and MPhil degrees from Columbia Universityin the USA and a PhD from the School of Informatics at the University of Edinburgh in the UK.

Sertac Bayhan is a principal scientist at Qatar Environment and Energy Research Institute (QEERI) and a Professor at Gazi University. His research encompasses power electronics and their applications in next-generation power and energy systems. He is the recipient of many prestigious international awards, such as the Teaching Excellence Award from Texas A&M Universityand a Research Fellow Excellence Award. He has been elected as a delegate of the Energy Cluster in IES and serves as an associate editor for several key journals.

Dragan Boscovic is a professor at Arizona State University's W. P. Carey Business School, USA. He also serves as the research director of the AZ Blockchain Applied Research Center and as CEO and founder of VizLore Group, a tech company specializing in pioneering IoT, data analytics, blockchain distributed computing and digital asset management. He has 24 patents issued to his name and a track record in conceiving strategies and overseeing development, investment, and innovation efforts.

Table of Contents

• Chapter 1: Introduction
• Part I: Fundamentals
  • Chapter 2: Sensor-based collection of solar and meteorological data
  • Chapter 3: Synthetic data generation through power hardware-in-the-loop (PHIL) simulations
  • Chapter 4: Data generation through digital twins
  • Chapter 5: Data wrangling
  • Chapter 6: Machine learning
  • Chapter 7: Game theory and AI for strategic energy management
  • Chapter 8: Edge to cloud
  • Chapter 9: AI in energy management: the market view
  • Chapter 10: Federated learning for energy management applications
  • Chapter 11: Quantum computing for energy management: a semi-non-technical guide for practitioners
• Part II: Applications
  • Chapter 12: Mapping all-sky images to GHI measurements for intra-hour solar forecasting
  • Chapter 13: Real-time measurement of electrical signal in medium-voltage distribution network using synchrophasor technology
  • Chapter 14: AI-powered power conversion
  • Chapter 15: Empowering resilience: AI and the future of microgrids
  • Chapter 16: Building trust by design through explainable AI for resilient and cognitive smart grids
  • Chapter 17: Electric mobility integration: a deep dive into AI solutions
  • Chapter 18: Optimization problems related to electric vehicle adoption
  • Chapter 19: Predictive photovoltaic maintenance strategies
  • Chapter 20: AI and robotic techniques for PV inspection
  • Chapter 21: Toward a blockchain-based smart microgrid: a peer-to-peer renewable energy trading framework
  • Chapter 22: Conclusions