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AI and Digitalization in Energy Management: (Energy Engineering)

AI and Digitalization in Energy Management: (Energy Engineering)

          
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About the Book

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.

Table of Contents:
Chapter 1: Introduction Chapter 2: Sensor-based Collection of Solar and Meteorological Data Chapter 3: Synthetic Data Generation Through 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: Semi Non-Technical Guide for Practitioners Chapter 12: Mapping All-Sky Images to GHI Measurements for Intra-hour Solar Forecasting Chapter 13: Realtime 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: Towards a Blockchain-based Smart Microgrid: A Peer to Peer Renewable Energy Trading Framework Chapter 22: Conclusions


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Product Details
  • ISBN-13: 9781839539794
  • Publisher: Institution of Engineering and Technology
  • Publisher Imprint: Institution of Engineering and Technology
  • Height: 234 mm
  • Series Title: Energy Engineering
  • ISBN-10: 1839539798
  • Publisher Date: 01 Sep 2025
  • Binding: Hardback
  • Language: English
  • Width: 156 mm


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