Artificial intelligence and internet of things for renewable energy systems / Neeraj Priyadarshi (2022)

Public ISBD Titre : Artificial intelligence and internet of things for renewable energy systems Type de document : document électronique Auteurs : Neeraj Priyadarshi, Auteur ; Sanjeevikumar Padmanaban, Auteur ; Kamal-Kant Hiran, Auteur ; Jens Bo Holm-Nielsen, Auteur Editeur : Berlin : De Gruyter Année de publication : 2022 Collection : Frontiers in Computational Intelligence num. Vol. 12 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-3-11-071404-3 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Bibliogr. .- Index Langues : Anglais (eng) Mots-clés : Artificial intelligence Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems. Note de contenu : Summary : 1. Artificial intelligence and internet of things for renewable energy systems 2. Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller 3. An IoT-based approach for efficient home automation 4. Design and implementation of IoT-enabled smart single-phase energy meter monitoring system 5. Internet of things (IoT)-based smart grids ... Artificial intelligence and internet of things for renewable energy systems [document électronique] / Neeraj Priyadarshi, Auteur ; Sanjeevikumar Padmanaban, Auteur ; Kamal-Kant Hiran, Auteur ; Jens Bo Holm-Nielsen, Auteur . - Berlin : De Gruyter, 2022 . - 1 fichier PDF : ill.. - (Frontiers in Computational Intelligence; Vol. 12) . ISBN : 978-3-11-071404-3 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Bibliogr. .- Index Langues : Anglais (eng) Mots-clés : Artificial intelligence Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems. Note de contenu : Summary : 1. Artificial intelligence and internet of things for renewable energy systems 2. Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller 3. An IoT-based approach for efficient home automation 4. Design and implementation of IoT-enabled smart single-phase energy meter monitoring system 5. Internet of things (IoT)-based smart grids ...

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Data science for supply chain forecasting / Nicolas Vandeput (2021)

Public ISBD Titre : Data science for supply chain forecasting Type de document : texte imprimé Auteurs : Nicolas Vandeput, Auteur Mention d'édition : 2nd ed Editeur : Berlin : De Gruyter Année de publication : 2021 Importance : XXVIII, 282 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-3-11-067110-0 Note générale : Bibliogr. p. [273] - 276. Glossaire. Index Langues : Anglais (eng) Mots-clés : Forecasting techniques Supply chain Business intelligence Data mining Index. décimale : 004.62:658.7 Traitement de l'information (Data science) pour la supply chain Résumé : Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. Note de contenu : Summary : Part I: Statistical forecasting. Part II: Machine learning. Part III: Data-Driven forecasting process management. Data science for supply chain forecasting [texte imprimé] / Nicolas Vandeput, Auteur  . -  2nd ed . - Berlin : De Gruyter, 2021 . - XXVIII, 282 p. : ill. ; 24 cm. ISBN : 978-3-11-067110-0 Bibliogr. p. [273] - 276. Glossaire. Index Langues : Anglais (eng) Mots-clés : Forecasting techniques Supply chain Business intelligence Data mining Index. décimale : 004.62:658.7 Traitement de l'information (Data science) pour la supply chain Résumé : Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. Note de contenu : Summary : Part I: Statistical forecasting. Part II: Machine learning. Part III: Data-Driven forecasting process management.

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059127	004.62:658.7 VAN	Papier	Bibliothèque Centrale	Management - Gestion	Disponible	Consultation sur place
059128	004.62:658.7 VAN	Papier	Bibliothèque Centrale	Management - Gestion	Disponible	En bon état