- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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.
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
