AI and Digitalization in Energy Management examines how digital technologies and artificial intelligence are transforming the planning, operation, and optimization of modern energy systems. With renewable energy at the core of the transition to a decarbonized future, the challenge of intermittency makes effective energy management more critical than ever.
Edited by a team of senior scientists with extensive project and industry experience, the book provides a systematic overview of methods, applications, and economic considerations in digital energy management.
Topics covered include:
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Solar and meteorological data collection and simulation
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Digital twins and data wrangling
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Machine learning, game theory, and AI for energy optimization
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Edge-to-cloud solutions, federated learning, and quantum computing in energy systems
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Intra-hour solar forecasting and synchrophasor technology
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AI-powered energy conversion, resilience, and explainable AI
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Electric mobility integration and EV adoption strategies
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Predictive PV maintenance, AI and robotics for PV inspection
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Blockchain-enabled microgrids and decentralized energy management
This book is an essential resource for researchers, energy engineers, and grid operators, as well as policy makers and advanced students interested in the intersection of clean energy, digitalization, and AI.
