| Description |
1 online resource (383 pages) |
| Contents |
Intro -- Preface -- Contents -- About the Editor -- 1 Prologue: Artificial Intelligence for Energy Transition -- 1.1 Energy Transition: Definition, Motivation, and Challenges -- 1.2 Artificial Intelligence for Energy Transition -- 1.3 Beyond State-of-the-Art: Contents of the Book -- 1.3.1 Chapter 2: Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management -- 1.3.2 Chapter 3: Automated Demand Side Management in Buildings: Lessons from Practical Trials |
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1.3.3 Chapter 4: A Multi-Agent Approach to Energy Optimization for Demand-Response Ready Buildings -- 1.3.4 Chapter 5: A Review on Non-Intrusive Load Monitoring Approaches Based on Machine Learning -- 1.3.5 Chapter 6: Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems -- 1.3.6 Chapter 7: Support Vector Machine Classification of Current Data for Fault Diagnosis and Similarity-Based Approach for Failure Prognosis in Wind Turbine Systems -- 1.3.7 Chapter 8: Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation |
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1.3.8 Chapter 9: How Machine Learning Can Support Cyber-Attack Detection in Smart Grids -- 1.3.9 Chapter 10: Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization -- 1.3.10 Chapter 11: Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems -- 1.3.11 Chapter 12: Data-Driven Predictive Flexibility Modeling of Distributed Energy Resources -- 1.3.12 Chapter 13: Applications of Artificial Neural Networks in the Context of Power Systems -- References |
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Part I Artificial Intelligence for Smart Energy Management -- 2 Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management -- 2.1 Introduction -- 2.2 Problem Formulation -- 2.3 Principle and Methodology -- 2.3.1 Data Project and Construction -- 2.3.2 Physical System and Data Connection -- 2.3.3 Building Thermal Modeling -- 2.3.3.1 Artificial Neural Networks Model -- 2.3.3.2 Input-Output Mapping -- 2.3.4 Smart Interactive System Design -- 2.3.4.1 SBEM System Architecture -- 2.3.4.2 Graphical User Interface Design |
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2.3.4.3 Users Recommendation Formulation -- 2.4 Experimental Results -- 2.4.1 Case Studies -- 2.4.2 MLP-Thermal Model Identification and Validation -- 2.4.3 Smart Interactive Interface -- 2.5 Conclusion and Perspectives -- References -- 3 Automated Demand Side Management in Buildings -- 3.1 Introduction -- 3.1.1 Decarbonization -- 3.1.2 Electrification -- 3.1.3 Optimization: The Need for Demand Side Management -- 3.1.4 Demand Reduction -- 3.1.4.1 Demand Response -- 3.2 Artificial Intelligence and DSM -- 3.2.1 User Engagement -- 3.2.1.1 Engagement on Investments -- 3.2.1.2 Engagement on Operation |
| Summary |
This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.). Uses examples and applications to facilitate the understanding of AI techniques for scalable energy transitions Includes examples, problems, and techniques in order to increase transparency and understanding of the methodological concepts Dedicated to researchers, practitioners, and operators working with industrial systems |
| Notes |
3.2.1.3 Discussion on Data-Driven User Engagement |
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Print version record |
| Subject |
Artificial intelligence -- Engineering applications.
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Power resources -- Data processing
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Artificial intelligence.
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Data mining.
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Business mathematics & systems.
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Communications engineering -- telecommunications.
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Technology & Engineering -- Engineering (General)
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Computers -- Intelligence (AI) & Semantics.
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Computers -- Database Management -- Data Mining.
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Business & Economics -- Industries -- Computer Industry.
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Technology & Engineering -- Telecommunications.
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Artificial intelligence -- Engineering applications
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Power resources -- Data processing
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| Form |
Electronic book
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| Author |
Sayed-Mouchaweh, Moamar
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| ISBN |
9783030427269 |
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3030427269 |
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