Articles liés à Intelligent Energy Demand Forecasting
Synopsis
This book offers approaches and methods to calculate optimal electric energy allocation, using evolutionary algorithms and intelligent analytical tools to improve the accuracy of demand forecasting. Focuses on improving the drawbacks of existing algorithms.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Wei-Chiang Samuelson Hong is an associate professor in the Department of Information Management at the Oriental Institute of Technology, Taiwan. His research interests mainly include computational intelligence (neural networks and evolutionary computation), and application of forecasting technology (ARIMA, support vector regression, and chaos theory), and tourism competitiveness evaluation and management. Dr. Hong's articles have been published in Applied Math-ematics and Computation, Applied Mathematical Modelling, Applied Soft Computing, Control and Cybernetics, Decision Support Systems, Electric Power Systems Research, Energy, Energies, Energy Conversion and Management, Energy Policy, IEEE Transactions on Fuzzy Systems, International Journal of Electrical Power & Energy Systems, Journal of Combinatorial Optimization, Journal of Systems and Software, Journal of Systems Engineering and Electronics, Mathematical Problems in Engineering, Neural Computing and Applications, and Neurocomputing, among others. Dr. Hong is currently on the editorial board of several journals, including International Journal of Applied Evolutionary Computation, Neurocomputing, Applied Soft Computing, Mathematical Problems in Engineering, and Energy Sources Part B: Economics, Planning, and Policy.
Dr. Hong serves as the program committee of various international conferences including premium ones such as IEEE CEC, IEEE CIS, IEEE ICNSC, IEEE SMC, IEEE CASE, and IEEE SMCia, etc.. In May 2012, his paper had been evaluated as "Top Cited Article 2007-2011" by Elsevier Publisher (Netherlands). In Sep. 2012, once again, his paper had been indexed in ISI Essential Science Indicator database as Highly Cited Papers, in the meanwhile, he also had been awarded as the Model Teacher Award by Taiwan PrivateEducation Association.
Dr. Hong is a senior member of IIE and IEEE. He is indexed in the list of Who's Who in the World (25th-30th Editions), Who's Who in Asia (2nd Edition), and Who's Who in Science and Engineering (10th and 11th Editions).
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Intelligent Energy Demand Forecasting
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Springer London, 2013
ISBN 10 : 144714967X
ISBN 13 : 9781447149675
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Intelligent Energy Demand Forecasting
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools Illustrates how hybrid evolutionary algorithms and some new approaches are improving existing algorithms . N° de réf. du vendeur 4185117
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Intelligent Energy Demand Forecasting
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Springer London Mrz 2013, 2013
ISBN 10 : 144714967X
ISBN 13 : 9781447149675
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools. 204 pp. Englisch. N° de réf. du vendeur 9781447149675
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Intelligent Energy Demand Forecasting
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Springer London, Springer London Mär 2013, 2013
ISBN 10 : 144714967X
ISBN 13 : 9781447149675
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Buch. Etat : Neu. Neuware -As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand.Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms.Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methodsto provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch. N° de réf. du vendeur 9781447149675
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Intelligent Energy Demand Forecasting (Lecture Notes in Energy, 10)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Intelligent Energy Demand Forecasting
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Springer London, Springer London, 2013
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ISBN 13 : 9781447149675
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools. N° de réf. du vendeur 9781447149675
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Intelligent Energy Demand Forecasting
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Springer London Ltd, 2013
ISBN 10 : 144714967X
ISBN 13 : 9781447149675
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Hardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 509. N° de réf. du vendeur C9781447149675
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Intelligent Energy Demand Forecasting (Lecture Notes in Energy, 10)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Etat : New. N° de réf. du vendeur ABLIING23Mar2411530317079
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Intelligent Energy Demand Forecasting (Lecture Notes in Energy, 10)
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Springer, 2013
ISBN 10 : 144714967X
ISBN 13 : 9781447149675
Ancien ou d'occasion
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Hardcover. Etat : Like New. Like New. book. N° de réf. du vendeur ERICA773144714967X6
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