1st Edition

Introduction to AI Techniques for Renewable Energy System

    422 Pages 196 B/W Illustrations
    by CRC Press

    Continue Shopping

    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.


    • 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.

    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



    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.