03569cam a22003738i 450000100090000000300050000900500170001400800410003101000170007202000250008904000290011404200080014305000210015108200290017224501120020125000190031326300090033226400580034130000290039933600260042833700280045433800270048250400510050950517630056052004770232365000300280065000370283065000290286770000350289670000370293170000300296870000400299877601570303821874241CSPC20250306153541.0210121s2021    flu      b    001 0 eng    a  2021001773  a9780367610920q(hbk)  aDLCbengerdacDLCdCSPC  apcc00aTJ808b.I66 202100a621.042028563bIn89122300aIntroduction to AI techniques for renewable energy system /cedited by Suman Lata Tripathi, [and 3 others].  aFirst edition.  a2106 1aBoca Raton, FL ;aAbingdon, Oxon :bCRC Press,c2021.  axii, 410 pages ;c24 cm.  atextbtxt2rdacontent  aunmediatedbn2rdamedia  avolumebnc2rdacarrier  aIncludes bibliographical references and index.0 aArtificial 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.  a"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"--cProvided by publisher. 0aRenewable energy sources. 0aPower-plantsxAutomatic control. 0aArtificial intelligence.1 aTripathi, Suman Lata,eeditor.1 aDubey, Mithilesh Kumar,eeditor.1 aRishiwal, Vinay,eeditor.1 aPadmanaban, Sanjeevikumar,eeditor.08iOnline version:tIntroduction to AI techniques for renewable energy systembFirst editiondBoca Raton : CRC Press, 2021z9781003104445w(DLC) 2021001774