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Artificial Intelligence Techniques for a Scalable Energy Transition

Advanced Methods, Digital Technologies, Decision Support Tools, and Applications

  • Book
  • © 2020

Overview

Editors:
  1. Moamar Sayed-Mouchaweh

    1. Institute Mines-Telecom Lille Douai, Douai, France

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  • 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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-ix
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  2. Prologue: Artificial Intelligence for Energy Transition

    • Moamar Sayed-Mouchaweh
    Pages 1-12

  3. Artificial Intelligence for Smart Energy Management

    1. Front Matter

      Pages 13-13
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    2. Large-Scale Building Thermal Modeling Based on Artificial Neural Networks: Application to Smart Energy Management

      • Lala Rajaoarisoa
      Pages 15-44

    3. Automated Demand Side Management in Buildings

      • Hussain Kazmi, Johan Driesen
      Pages 45-76

    4. A Multi-Agent Approach to Energy Optimisation for Demand-Response Ready Buildings

      • Oudom Kem, Feirouz Ksontini
      Pages 77-107

    5. A Review on Non-intrusive Load Monitoring Approaches Based on Machine Learning

      • Hajer Salem, Moamar Sayed-Mouchaweh, Moncef Tagina
      Pages 109-131

  4. Artificial Intelligence for Reliable Smart Power Systems

    1. Front Matter

      Pages 133-133
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    2. Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems

      • Gerasimos Rigatos, Dimitrios Serpanos, Vasilios Siadimas, Pierluigi Siano, Masoud Abbaszadeh
      Pages 135-156

    3. Support Vector Machine Classification of Current Data for Fault Diagnosis and Similarity-Based Approach for Failure Prognosis in Wind Turbine Systems

      • Samir Benmoussa, Mohand Arab Djeziri, Roberto Sanchez
      Pages 157-182

    4. Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

      • Mohand Arab Djeziri, Samir Benmoussa, Enrico Zio
      Pages 183-223

    5. How Machine Learning Can Support Cyberattack Detection in Smart Grids

      • Bruno Bogaz Zarpelão, Sylvio Barbon Jr., Dilara Acarali, Muttukrishnan Rajarajan
      Pages 225-258

  5. Artificial Intelligence for Control of Smart Appliances and Power Systems

    1. Front Matter

      Pages 259-259
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    2. Neurofuzzy Approach for Control of Smart Appliances for Implementing Demand Response in Price Directed Electricity Utilization

      • Miltiadis Alamaniotis, Iosif Papadakis Ktistakis
      Pages 261-278

    3. Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems

      • Franz Wotawa
      Pages 279-310

    4. Data-Driven Predictive Flexibility Modeling of Distributed Energy Resources

      • Indrasis Chakraborty, Sai Pushpak Nandanoori, Soumya Kundu, Karanjit Kalsi
      Pages 311-343

    5. Applications of Artificial Neural Networks in the Context of Power Systems

      • Jan-Hendrik Menke, Marcel Dipp, Zheng Liu, Chenjie Ma, Florian Schäfer, Martin Braun
      Pages 345-373

  6. Back Matter

    Pages 375-382
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Keywords

About this book

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.).

Editors and Affiliations

  • Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

About the editor

Moamar Sayed-Mouchaweh received his Master degree from the University of Technology of Compiegne-France in 1999. Then, he received his PhD degree from the University of Reims-France in December 2002. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research center in Sciences and Technology of the Information and the Communication (CReSTIC). In December 2008, he obtained the Habilitation to Direct Researches (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines “Ecole Nationale Supérieure des Mines de Douai” at the Department of Computer Science and Automatic Control (Informatique & Automatique). He edited the Springer book ‘Learning in Non-Stationary Environments: Methods and Applications ‘, in April 2012 and wrote two Brief Springer books in Electrical and Computer Engineering: ‘Discrete Event Systems: Diagnosis and Diagnosability’, and ‘Learning from Data Streams in Dynamic Environments’. He was a guest editor of several special issues of international journals. He was IPC Chair of the 12th IEEE International Conference on Machine Learning and Applications (ICMLA'13), the Conference Chair and IPC Chair of IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS2015), and the IPC Chair of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA'16). He is working as a member of the Editorial Board of Elsevier Journal “Applied Soft Computing” and Springer Journals “Evolving systems” and “Intelligent Industrial Systems”.

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