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Introduction to AI techniques for renewable energy system / edited by Suman Lata Tripathi, [and 3 others].

Contributor(s): Material type: TextTextPublisher: Boca Raton, FL ; Abingdon, Oxon : CRC Press, 2021Edition: First editionDescription: xii, 410 pages ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780367610920
Subject(s): Additional physical formats: Online version:: Introduction to AI techniques for renewable energy systemDDC classification:
  • 621.042028563 In891 23
LOC classification:
  • TJ808 .I66 2021
Contents:
Artificial intelligence : a new era in renewable energy systems -- Role of AI in renewable energy management -- AI-based renewable energy with emerging applications: issues and challenges -- Foundations of machine learning -- Introduction of AI techniques and approaches -- A comprehensive overview of hybrid renewable energy systems -- Dynamic modeling and performance analysis of switched-mode controller for hybrid energy systems -- Artificial intelligence and machine learning methods for renewable energy -- Artificial neural network-based power optimizer for solar photovoltaic system: an integrated approach with genetic algorithm -- Predictive maintenance: AI behind equipment failure prediction -- AI techniques for the challenges in smart energy systems -- Energy efficiency -- Renewable (bio-based) energy from natural resources (plant biomass matters) --Evolving trends for smart grid using artificial intelligent techniques -- Introduction to AI techniques for photovoltaic energy conversion system -- Deep learning-based fault identification of microgrid transformers -- Power quality improvement for grid-integrated renewable energy sources: a comparative analysis of UPQC topologies -- AI-based energy-efficient fault mitigation technique for reliability enhancement of wireless sensor network -- AI techniques applied to wind energy -- Comparative performance analysis of multi-objective metaheuristic approaches for parameter identification of three-diode-modeled photovoltaic cells -- Artificial intelligence techniques in smart grid -- Parameter identification of a new reverse two-diode model by moth flame optimizer -- Time series energy prediction and improved decision-making -- Machine learning-enabled cyber security in smart grids.
Summary: "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"-- Provided by publisher.
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Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books Main Library Engineering Section ENG 621.042028563 In891 2021 (Browse shelf(Opens below)) 1-1 Available 029099

Includes bibliographical references and index.

Artificial intelligence : a new era in renewable energy systems -- Role of AI in renewable energy management -- AI-based renewable energy with emerging applications: issues and challenges -- Foundations of machine learning -- Introduction of AI techniques and approaches -- A comprehensive overview of hybrid renewable energy systems -- Dynamic modeling and performance analysis of switched-mode controller for hybrid energy systems -- Artificial intelligence and machine learning methods for renewable energy -- Artificial neural network-based power optimizer for solar photovoltaic system: an integrated approach with genetic algorithm -- Predictive maintenance: AI behind equipment failure prediction -- AI techniques for the challenges in smart energy systems -- Energy efficiency -- Renewable (bio-based) energy from natural resources (plant biomass matters) --Evolving trends for smart grid using artificial intelligent techniques -- Introduction to AI techniques for photovoltaic energy conversion system -- Deep learning-based fault identification of microgrid transformers -- Power quality improvement for grid-integrated renewable energy sources: a comparative analysis of UPQC topologies -- AI-based energy-efficient fault mitigation technique for reliability enhancement of wireless sensor network -- AI techniques applied to wind energy -- Comparative performance analysis of multi-objective metaheuristic approaches for parameter identification of three-diode-modeled photovoltaic cells -- Artificial intelligence techniques in smart grid -- Parameter identification of a new reverse two-diode model by moth flame optimizer -- Time series energy prediction and improved decision-making -- Machine learning-enabled cyber security in smart grids.

"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"-- Provided by publisher.

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