Energy Management—Collective and Computational Intelligence with Theory and Applications: Studies in Systems, Decision and Control, cartea 149
Editat de Cengiz Kahraman, Gülgün Kayakutluen Limba Engleză Hardback – 4 apr 2018
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Specificații
ISBN-13: 9783319756899
ISBN-10: 3319756893
Pagini: 590
Ilustrații: XV, 554 p. 121 illus., 69 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.97 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Systems, Decision and Control
Locul publicării:Cham, Switzerland
ISBN-10: 3319756893
Pagini: 590
Ilustrații: XV, 554 p. 121 illus., 69 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.97 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Systems, Decision and Control
Locul publicării:Cham, Switzerland
Cuprins
Complexity in Energy Systems.- Fuzzy Sets Applications in Complex Energy Systems: A literature review.- Fuzzy Forecasting Methods for Energy Planning.- Wind Energy Investment Analyses Based on Fuzzy Sets.- Strategic Analysis of Solar Energy Pricing Process with Hesitant Fuzzy Cognitive Map.- Electrical Vehicle charging coordination algorithms framework.
Textul de pe ultima copertă
This book presents a selection of recently developed collective and computational intelligence techniques, which it subsequently applies to energy management problems ranging from performance analysis to economic analysis, and from strategic analysis to operational analysis, with didactic numerical examples. As a form of intelligence emerging from the collaboration and competition of individuals, collective and computational intelligence addresses new methodological, theoretical, and practical aspects of complex energy management problems. The book offers an excellent reference guide for practitioners, researchers, lecturers and postgraduate students pursuing research on intelligence in energy management. The contributing authors are recognized researchers in the energy research field.
Caracteristici
Presents cases and examples of collective and computational intelligence applications in the energy industry Includes collective and computational intelligence techniques such as ant colony optimization, swarm optimization, simulated annealing, genetic algorithms, neural networks, and fuzzy sets Offers essential insights for the investigation and projection of energy plans and schedules using the most recent analytical methods from collective and computational intelligence