LEADER 05112nam 2200529 450 001 9910831039003321 005 20231110224705.0 010 $a1-119-76172-7 010 $a1-119-76168-9 010 $a1-119-76171-9 024 7 $a10.1002/9781119761686 035 $a(MiAaPQ)EBC6876112 035 $a(Au-PeEL)EBL6876112 035 $a(CKB)21022295600041 035 $a(OCoLC)1301543087 035 $a(OCoLC-P)1301543087 035 $a(CaSebORM)9781119761693 035 $a(EXLCZ)9921022295600041 100 $a20220919d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial intelligence for renewable energy systems /$fedited by S. Balamurugan [and three others] 210 1$aHoboken, New Jersey ;$aBeverly, Massachusetts :$cScrivener Publishing :$cJohn Wiley & Sons, Inc.,$d[2022] 210 4$dİ2022 215 $a1 online resource (270 pages) 225 0 $aArtificial intelligence and soft computing for industrial transformation 311 08$aPrint version: Vyas, Ajay Kumar Artificial Intelligence for Renewable Energy Systems Newark : John Wiley & Sons, Incorporated,c2022 9781119761693 320 $aIncludes bibliographical references and index. 327 $aFront Matter -- Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation / Arif Iqbal, Girish Kumar Singh -- Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource / N Vinay, Ajay Sudhir Bale, Subhashish Tiwari, Chithra R Baby -- Artificial Intelligence-Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network / Nitesh Chouhan -- Artificial Intelligence for Modeling and Optimization of the Biogas Production / Narendra Khatri, Kamal Kishore Khatri -- Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression / Siddhi Vinayak Pandey, Jeet Patel, Harsh S Dhiman -- Deep Learning Algorithms for Wind Forecasting: An Overview / M Lydia, G Edwin Prem Kumar -- Deep Feature Selection for Wind Forecasting-I / C Ramakrishnan, S Sridhar, Kusumika Krori Dutta, R Karthick, C Janamejaya -- Deep Feature Selection for Wind Forecasting-II / S Oswalt Manoj, JP Ananth, Balan Dhanka, Maharaja Kamatchi -- Data Falsification Detection in AMI: A Secure Perspective Analysis / VV Vineeth, S Sophia -- Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques / Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta, Akshay Srivastava -- Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy / Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini, Dinesh Kumar Saini -- Index -- Also of Interest 330 $aARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology. 410 0$aArtificial Intelligence and Soft Computing for Industrial Transformation 606 $aRenewable energy sources$xData processing 606 $aArtificial intelligence$xEngineering applications 615 0$aRenewable energy sources$xData processing. 615 0$aArtificial intelligence$xEngineering applications. 676 $a363.70028563 702 $aBalamurugan$b S.$cProf., 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831039003321 996 $aArtificial intelligence for renewable energy systems$94121593 997 $aUNINA