| Description |
1 online resource (xiv, 299 pages) : illustrations (some color) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Front Cover -- Intelligent Learning Approaches for Renewable and Sustainable Energy -- Copyright Page -- Contents -- List of contributors -- Preface -- Section I: Introduction to intelligent learning approaches for renewable and sustainable energy -- Section II: Applications of intelligence learning approaches for renewable and sustainable energy -- Section III: Intelligent learning methods for optimizing integrated energy systems -- I. Introduction to intelligent learning approaches for renewable and sustainable energy |
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1 Transforming the grid: AI, ML, renewable, storage, EVs, and prosumers -- 1.1 Introduction -- 1.2 Artificial intelligence and machine learning in the modern grid -- 1.2.1 AI-based load forecasting -- 1.2.2 AI-based renewable energy forecasting -- 1.2.3 EVs operation, AI, and modern grid integration -- 1.2.4 AI in modern grid fault diagnostics -- 1.3 Status of RES and storage systems in the modern grid -- 1.3.1 Status of RES in the modern grid -- 1.3.1.1 Solar energy -- 1.3.1.2 Wind energy -- 1.3.1.3 Other renewable energy sources -- 1.3.2 Status of storage systems in the modern grid |
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1.4 Case study: application of AI in power electronics driven RES -- 1.4.1 Problem formulation -- 1.4.2 System under investigation -- 1.4.3 Genetic algorithm for data generation -- 1.4.3.1 Objective function -- 1.4.4 ANN-based controller -- 1.4.5 Results -- References -- 2 A new artificial intelligence-based demand side management method for EV charging stations -- 2.1 Introduction -- 2.1.1 Direct load control -- 2.2 Problem description -- 2.3 Proposed method -- 2.3.1 RUS Boost tree ensemble classifiers -- 2.4 Conclusion -- References |
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3 Modeling stochastic renewable energy processes by combining the Monte Carlo method and mixture density networks -- 3.1 Introduction to stochastic phenomena in renewable energies -- 3.2 Monte Carlo method (MCM) -- 3.2.1 Foundations -- 3.2.2 Algorithms -- 3.2.3 Advantages and shortcomings -- 3.2.4 Applications to renewable energies -- 3.3 Mixture density networks -- 3.3.1 Foundations of machine learning -- 3.3.2 Gaussian distribution and Gaussian mixture -- 3.3.3 MDN architecture -- 3.3.4 MDN training -- 3.3.5 Applications to the renewable energies -- 3.4 Case study -- 3.4.1 Formulation |
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3.4.2 MDN-based modeling -- 3.4.3 Monte Carlo simulation -- 3.4.4 Analysis of the results -- 3.5 Concluding remarks -- Acknowledgments -- References -- 4 Profitability and performance improvement of smart photovoltaic/energy storage microgrid by integration of solar produ... -- 4.1 Introduction -- 4.2 Forecasting of solar radiation and PV production -- 4.2.1 Brief overview of the forecasting methods for solar radiation -- 4.2.2 Time series based forecasting methods -- 4.2.2.1 Cleaning the data (making it stationary) -- 4.2.2.2 Persistence and smart (or scaled) persistence -- 4.2.2.3 ARMA model. |
| Summary |
Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy.The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence. Explores cutting-edge intelligent techniques and their implications for future energy systems development Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more Includes a range of case studies that provide insights into the challenges and solutions in real-world applications. |
| Note |
Description based on online resource; title from digital title page (viewed on April 29, 2024). |
| Subject |
Renewable energy sources -- Data processing.
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Artificial intelligence.
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Énergies renouvelables -- Informatique.
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Intelligence artificielle.
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artificial intelligence.
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| Added Author |
Guerrero, Josep, editor.
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Gupta, Pankaj, editor.
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Kandari, Ritu, editor.
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Micallef, Alexander, editor.
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| Other Form: |
Print version: 0443158061 9780443158063 (OCoLC)1389607308 |
| ISBN |
9780443158070 electronic book |
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044315807X electronic book |
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9780443158063 |
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0443158061 |
| Standard No. |
AU@ 000076170783 |
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AU@ 000076484954 |
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