Introduction to AI Techniques for Renewable Energy System

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.

Chapter 1: Artificial Intelligence: A New Era in Renewable Energy SystemsKandra 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 ChallengesOmkar Singh, Mano Yadav, Preeti Yadav, Vinay Rishiwal Chapter 4: Foundations of Machine LearningNeeta Nathani, Abhishek Singh Chapter 5: Introduction of AI techniques and ApproachesNamrata Dhanda, Rajat Verma Chapter 6: A Comprehensive Overview of Hybrid Renewable Energy SystemsAmit 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 SystemsSS. Linnet Jaya, V. Kirubakaran Chapter 8: Artificial Intelligence and Machine Learning Methods for Renewable EnergySushila Palwe, Prerna Lahane Chapter 9: Artificial Neural Network Based Power Optimizer for Solar Photovoltaic System: An Integrated Approach with Genetic AlgorithmS.R.Revathy, V.Kirubakaran Chapter 10: Predictive Maintenance: AI Behind Equipment Failure PredictionS.Sharanya, Revathi Venkataraman, G. Murali Chapter 11: AI Techniques for the Challenges in Smart Energy SystemsS. Dwivedi Chapter 12: Energy EfficiencyHar Lal Singh, Sarita Khaturia1 and Mamta Chahar Chapter 13: Renewable Energy from Plant Biomass and Photosynthetic Organisms and its OperationsRajesh K. Srivastava Chapter 14: Evolving Trends for Smart Grid Using Artificial Intelligent TechniquesPooja Yadav, Prakhar Chaudhary, Hemant Yadav Chapter 15: Introduction to AI techniques for Photovoltaic Energy Conversion SystemSiddharth Joshi, Nirav Karelia Chapter 16: Deep Learning Based Fault Identification of Micro Grid TransformersS. 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 NetworkSyed Mufassir Yaseen, Mithilesh Kumar Dubey, Majid Charoo Chapter 19: AI Techniques Applied to Wind EnergySwagat Kumar Samantaray, Shasanka Sekhar Rout Chapter 20: Comparative Performance Analysis of Multi-Objective Metaheuristic Approaches for Parameter Identification of Three-Diode Modelled Photovoltaic CellsSaumyadip Hazra, Souvik Ganguli Chapter 21: Artificial Intelligence Techniques in Smart GridIrtiqa 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 23Time-Series Energy Prediction and Improved Decision MakingIram 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.