Recent advances in energy harvesting technologies

This book discusses the application of artificial intelligence (AI) for energy harvesting.

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Bibliographic Details
Other Authors Rajput, Shailendra, Sharma, Abhishek, Jately, Vibhu, Ram, Mangey
Format Electronic eBook
LanguageEnglish
Published Milton : River Publishers, 2023.
SeriesRiver Publishers series in energy sustainability and efficiency
Subjects
Energy harvesting > Technological innovations.
Artificial intelligence > Engineering applications.
Artificial intelligence > Environmental aspects.
elektronické knihy
electronic books
Online AccessFull text
ISBN9781000962833
1000962830
9781003440383
100344038X
9781000962932
1000962938
8770228450
9788770228459
Physical Description1 online resource (253 p.).

Cover

Table of Contents:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • Acknowledgement
  • List of Figures
  • List of Tables
  • List of Contributors
  • List of Abbreviations
  • Chapter 1: AI in Energy Harvesting
  • 1.1: Introduction
  • 1.2: Energy from Mechanical Vibrations
  • 1.3: Fundamentals of Vibrational Energy Harvesting
  • 1.4: Piezoelectric and Triboelectric Nanogenerators
  • 1.5: Electro-mechanical Energy Harvesting
  • 1.6: Piezoelectric Energy Harvesters
  • 1.7: Triboelectric Energy Harvester (TENG)
  • 1.8: Artificial Intelligent in Energy Harvesting
  • 1.9: Artificial Intelligent in Energy Harvesters
  • 1.10: Philosophy of AI in Energy Harvesting
  • 1.11: Limitation and Future Scope of AI in Energy Harvesting
  • 1.12: Conclusion
  • Chapter 2: Application of the ANN Method in Water Energy Harvesting
  • 2.1: Introduction
  • 2.2: Soft Computing
  • 2.2.1: Soft-computing properties
  • 2.3: Artificial Neural Networks (ANN)
  • 2.4: Application of ANN in Hydrology
  • 2.4.1: In stream-flow modelling
  • 2.4.2: In water quality modelling
  • 2.4.3: In groundwater modelling
  • 2.5: Case Studies: Application of ANN in Water Resources
  • 2.5.1: Rainfall-runoff (RR) process
  • 2.5.2: Surface runoff
  • 2.5.3: Rainfall-runoff (RR) modelling approaches
  • 2.5.4: Physically based RR models
  • 2.5.5: Conceptual RR models
  • 2.5.6: Empirical RR models
  • 2.6: ANN Application in Water Energy Harvesting
  • 2.7: Rainfall-runoff (RR) Modelling using ANNs
  • 2.8: Implementation of ANN-RR (Rainfall-Runoff) Models
  • 2.9: Conclusion
  • Chapter 3: Artificial Intelligence (AI) in Electrical Vehicles
  • 3.1: Introduction
  • 3.2: Advantages of Electric Vehicles
  • 3.3: Opportunities for Energy Harvesting on Electric Vehicles
  • 3.4: The Electric Vehicle Industry and Artificial Intelligence
  • 3.5: How AI Is Accelerating the Power of Electric Vehicle Batteries
  • 3.6: Internal Combustion Engine (ICE) Sales Ban Proposals
  • 3.7: Electric Vehicle Charging
  • 3.8: ML and Predictive Analytics
  • 3.9: Supervised Learning
  • 3.10: Unsupervised Learning and Statistical Models
  • 3.11: Electric Vehicle Battery Makers
  • 3.12: Is Lithium the New Gasoline?
  • 3.13: Enabling High-Energy Dense Batteries
  • 3.14: Future Scope of AI in the EV Industry
  • 3.15: Conclusion
  • Chapter 4: Advances in Maximum Power Point Tracking of Solar Photovoltaic Systems Under Partially Shaded Conditions with Swarm Intelligence Techniques
  • 4.1: Introduction
  • 4.2: Model Description of PV Source
  • 4.3: Partial Shading and Its Effects
  • 4.4: Model Description of MPPT Controller
  • 4.5: Overview of MPPT Techniques for Solar Photovoltaic Systems
  • 4.6: Working Principles of MPPT Techniques
  • 4.6.1: Gradient-based MPPT techniques
  • 4.6.2: Soft computing-based MPPT techniques
  • 4.7: Classification of MPPT Techniques
  • 4.7.1: Gradient-based MPPT techniques

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