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Artificial Intelligence Techniques for a Scalable Energy Transition

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Cover of 'Artificial Intelligence Techniques for a Scalable Energy Transition'

Table of Contents

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    Book Overview
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    Chapter 1 Prologue: Artificial Intelligence for Energy Transition
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    Chapter 2 Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management
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    Chapter 3 Automated Demand Side Management in Buildings
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    Chapter 4 A Multi-Agent Approach to Energy Optimisation for Demand-Response Ready Buildings
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    Chapter 5 A Review on Non-intrusive Load Monitoring Approaches Based on Machine Learning
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    Chapter 6 Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems
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    Chapter 7 Support Vector Machine Classification of Current Data for Fault Diagnosis and Similarity-Based Approach for Failure Prognosis in Wind Turbine Systems
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    Chapter 8 Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation
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    Chapter 9 How Machine Learning Can Support Cyberattack Detection in Smart Grids
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    Chapter 10 Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization
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    Chapter 11 Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems
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    Chapter 12 Data-Driven Predictive Flexibility Modeling of Distributed Energy Resources
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    Chapter 13 Applications of Artificial Neural Networks in the Context of Power Systems
Attention for Chapter 3: Automated Demand Side Management in Buildings
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