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[null,"1.","Record Nr.",null,"UNISA990006166170203316"]
[null,null,"Autore",null,"LA TEGOLA, Antonio"]
[null,null,"Titolo",null,"Teoria semiprobabilistica della sicurezza nel calcolo agli stati limite delle strutture in cemento armato / Antonio La Tegola"]
[null,null,"Pubbl/distr/stampa",null,"Bari : Adriatica, 1977"]
[null,null,"Descrizione fisica",null,"299 p. ; 24 cm + [1] c. di tav. ripieg."]
[null,null,"Disciplina",null,"624.18341"]
[null,null,"Soggetti",null,"Strutture in cemento armato - Calcolo"]
[null,null,"Collocazione",null,"624.183 LAT"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910831039003321"]
[null,null,"Titolo",null,"Artificial intelligence for renewable energy systems / / edited by S. Balamurugan [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Scrivener Publishing : , : John Wiley & Sons, Inc., , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-119-76172-7"]
[{"$nulls":4},"1-119-76168-9"]
[{"$nulls":4},"1-119-76171-9"]
[null,null,"Descrizione fisica",null,"1 online resource (270 pages)"]
[null,null,"Collana",null,"Artificial intelligence and soft computing for industrial transformation"]
[null,null,"Disciplina",null,"363.70028563"]
[null,null,"Soggetti",null,"Renewable energy sources - Data processing"]
[{"$nulls":4},"Artificial intelligence - Engineering applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front 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 "]
[{"$nulls":4},"Pahwa, Manish Dadhich, Jaskaran Singh Saini, Dinesh Kumar Saini -- Index -- Also of Interest"]
[null,null,null,"Sommario/riassunto","ARTIFICIAL 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."]