Introduction to AI Techniques for Renewable Energy System
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Introduction to AI Techniques for Renewable Energy System
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Book Description
This book helps the undergraduate, graduate students and Academician to learn the concept of Artificial Intelligence techniques used in renewal energy with suitable real-life examples. 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 (e.g. inferior quality of data, in-sufficient long series, etc.). For overcoming these problems, AI techniques appear to be one of the most substantial parts of the book. The book summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. Book outlines selected AI applications for renewable energy. In particular, discusses methods using the AI approach for the following applications using suitable examples: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.
Key selling Features:
- The impact of the proposed book is to provide a significant area of concern to develop a foundation for the implementation process renewable energy system with intelligent techniques.
- The researchers working on a renewable energy system can correlate their work with intelligent and machine learning approaches.
- To make aware of the international standards for intelligent renewable energy systems design, reliability and maintenance.
- To give better incites of the solar cell, biofuels, wind and other renewable energy system design and characterization, including the equipment for smart energy systems.
Table of Contents
Chapter 1: Artificial Intelligence: A New Era in Renewable Energy Systems
Kandra Prameela1, Challa Lahari, Grandhi Sai Kishore, Kandula Venkata Nikhil, Pavuluri Hemanth
Chapter 2: Role of AI in Renewable Energy Management
Anupama Sharma, Sanjeev Kumar Prasad, Rashmi Chaudhary
Chapter 3: AI-based Renewable Energy with Emerging Applications: Issues and Challenges
Omkar Singh, Mano Yadav, Preeti Yadav, Vinay Rishiwal
Chapter 4: Foundations of Machine Learning
Neeta Nathani, Abhishek Singh
Chapter 5: Introduction of AI techniques and Approaches
Namrata Dhanda, Rajat Verma
Chapter 6: A Comprehensive Overview of Hybrid Renewable Energy Systems
Amit Kumer Podder, Muhammed Zubair Rahman, Sujon Mia and S M Fuad Hossain Fahim
Chapter 7: Dynamic Modelling and Performance Analysis of Switched-Mode Controller for Hybrid Energy SystemsS
S. Linnet Jaya, V. Kirubakaran
Chapter 8: Artificial Intelligence and Machine Learning Methods for Renewable Energy
Sushila Palwe, Prerna Lahane
Chapter 9: Artificial Neural Network Based Power Optimizer for Solar Photovoltaic System: An Integrated Approach with Genetic Algorithm
S.R.Revathy, V.Kirubakaran
Chapter 10: Predictive Maintenance: AI Behind Equipment Failure Prediction
S.Sharanya, Revathi Venkataraman, G. Murali
Chapter 11: AI Techniques for the Challenges in Smart Energy Systems
S. Dwivedi
Chapter 12: Energy Efficiency
Har Lal Singh, Sarita Khaturia1 and Mamta Chahar
Chapter 13: Renewable Energy from Plant Biomass and Photosynthetic Organisms and its Operations
Rajesh K. Srivastava
Chapter 14: Evolving Trends for Smart Grid Using Artificial Intelligent Techniques
Pooja Yadav, Prakhar Chaudhary, Hemant Yadav
Chapter 15: Introduction to AI techniques for Photovoltaic Energy Conversion System
Siddharth Joshi, Nirav Karelia
Chapter 16: Deep Learning Based Fault Identification of Micro Grid Transformers
S. Poornima
Chapter 17: Power Quality Improvement for Grid Integrated Renewable Energy Sources: A Comparative analysis of UPQC Topologies
Nirav Karelia, Amit Sant, Vivek Pandya
Chapter 18: AI based Energy Efficient Fault Mitigation Technique for Reliability Enhancement of Wireless Sensor Network
Syed Mufassir Yaseen, Mithilesh Kumar Dubey, Majid Charoo
Chapter 19: AI Techniques Applied to Wind Energy
Swagat Kumar Samantaray, Shasanka Sekhar Rout
Chapter 20: Comparative Performance Analysis of Multi-Objective Metaheuristic Approaches for Parameter Identification of Three-Diode Modelled Photovoltaic Cells
Saumyadip Hazra, Souvik Ganguli
Chapter 21: Artificial Intelligence Techniques in Smart Grid
Irtiqa Amin, Dr. Mithilesh Dubey
Chapter 22: Parameter Identification of a New Reverse Two Diode Model by Moth Flame Optimizer
Saumyadip Hazra, Souvik Ganguli, Suman Lata Tripathi
Chapter 23
Time-Series Energy Prediction and Improved Decision Making
Iram Naim, Tripti Mahara
Editor(s)
Biography
Suman Lata Tripathi is working as a Professor at School of Electronics and Electrical Engineering, Lovely Professional University, India.
Mithilesh Kumar Dubey is working as an Associate Professor at School of Computer Science and Engineering, Lovely Professional University, India.
Vinay Rishiwal is working as a Professor at Department of Computer Science and Information Technology, Faculty of Engineering and Technology, MJP Rohilkhand University, Bareilly, Uttar Pradesh, India.
Sanjeevikumar Padmanaban is working as a faculty member, at Department of Energy Technology, Aalborg University, Esbjerg, Denmark.