Introduction to AI Techniques for Renewable Energy System

✍ Scribed by Suman Lata Tripathi (editor), Mithilesh Kumar Dubey (editor), Vinay Rishiwal (editor), Sanjeevikumar Padmanaban (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 423
• Edition: 1
• Category: Library

✦ Synopsis

Introduction to AI techniques for Renewable Energy System

Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

Features

• Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques
• Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches
• Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance
• Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems

This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
About the Editors
Chapter 1: Artificial Intelligence: A New Era in Renewable Energy
Systems
Chapter 2: Role of AI in Renewable Energy Management
Chapter 3: AI-Based Renewable Energy with Emerging Applications:
Issues and Challenges
Chapter 4: Foundations of Machine Learning
Chapter 5: Introduction of AI Techniques and Approaches
Chapter 6: A Comprehensive Overview of Hybrid Renewable Energy
Systems
Chapter 7: Dynamic Modeling and Performance Analysis of Switched-
Mode Controller for Hybrid Energy Systems
Chapter 8: Artificial Intelligence and Machine Learning Methods for
Renewable Energy
Chapter 9: Artificial Neural Network-Based Power Optimizer for Solar Photovoltaic System: An Integrated Approach with Genetic Algorithm
Chapter 10: Predictive Maintenance: AI Behind Equipment Failure
Prediction
Chapter 11: AI Techniques for the Challenges in Smart Energy Systems
Chapter 12: Energy Efficiency
Chapter 13: Renewable (Bio-Based) Energy from Natural Resources (Plant Biomass Matters)
Chapter 14: Evolving Trends for Smart Grid Using Artificial Intelligent Techniques
Chapter 15: Introduction to AI Techniques for Photovoltaic Energy Conversion System
Chapter 16: Deep Learning-Based Fault Identification of Microgrid Transformers
Chapter 17: Power Quality Improvement for Grid-Integrated Renewable Energy Sources: A Comparative Analysis of UPQC Topologies
Chapter 18: AI-Based Energy-Efficient Fault Mitigation Technique for Reliability Enhancement of Wireless Sensor Network
Chapter 19: AI Techniques Applied to Wind Energy
Chapter 20: Comparative Performance Analysis of Multi-Objective Metaheuristic Approaches for Parameter Identification of Three-Diode-Modeled Photovoltaic Cells
Chapter 21: Artificial Intelligence Techniques in Smart Grid
Chapter 22: Parameter Identification of a New Reverse Two-Diode Model
by Moth Flame Optimizer
Chapter 23: Time Series Energy Prediction and Improved Decision-Making
Chapter 24: Machine Learning-Enabled Cyber Security in Smart Grids
Index