AI and Digitalization in Energy Management
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.
1193.50Lei
1193.50Lei
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Descrierea produsului
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.
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