| Description |
1 online resource |
| 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 |
| 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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| Other Form: |
Original 0443158061 9780443158063 (OCoLC)1389607308 |
| ISBN |
9780443158070 (electronic bk.) |
|
044315807X (electronic bk.) |
|
9780443158063 |
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0443158061 |
| Standard No. |
AU@ 000076170783 |
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