Intelligent learning aproaches for renewable and sustainable energy [electronic resource] / edited by Josep M. Guerrero, Pankaj Gupta, Ritu Kandari, Alexander Micallef.
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Online Access: |
Full Text (via ScienceDirect) |
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Format: | Electronic eBook |
Language: | English |
Published: |
Amsterdam :
Elsevier,
2024.
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Subjects: |
Table of 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
- 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
- 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
- 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
- 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