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Evolutionary Wind Turbine Placement Optimization with Geographical Constraints

Autor Daniel Lückehe
en Limba Engleză Paperback – 7 iun 2017
Daniel Lückehe presents different approaches to optimize locations of multiple wind turbines on a topographical map. The author succeeds in significantly improving placement solutions by employing optimization heuristics. He proposes various real-world scenarios that represent real planning situations. Advanced evolutionary heuristics for the turbine placement optimization create not only highly optimized solutions but also significantly different solutions to give decision-makers optimal choices. As a matter of fact, wind turbines play an important role towards green energy supply. An optimal location is essential to achieve the highest possible energy efficiency.   
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Specificații

ISBN-13: 9783658184643
ISBN-10: 3658184647
Pagini: 220
Ilustrații: XXII, 195 p. 64 illus., 15 illus. in color.
Dimensiuni: 148 x 210 x 13 mm
Greutate: 0.29 kg
Ediția:1st edition 2017
Editura: SpringerGabler
Locul publicării:Wiesbaden, Germany

Cuprins

Solving Optimization Problems.- Wind Prediction Model.- Geographical Planning Scenarios.- Constrained Placement Optimization.- Constraint Handling with Penalty Functions.- Advanced Evolutionary Heuristics.


Textul de pe ultima copertă

Daniel Lückehe presents different approaches to optimize locations of multiple wind turbines on a topographical map. The author succeeds in significantly improving placement solutions by employing optimization heuristics. He proposes various real-world scenarios that represent real planning situations. Advanced evolutionary heuristics for the turbine placement optimization create not only highly optimized solutions but also significantly different solutions to give decision-makers optimal choices. As a matter of fact, wind turbines play an important role towards green energy supply. An optimal location is essential to achieve the highest possible energy efficiency. 

Contents
  • Solving Optimization Problems
  • Wind Prediction Model 
  • Geographical Planning Scenarios
  • Constrained Placement Optimization
  • Constraint Handling with Penalty Functions
  • Advanced Evolutionary Heuristics
Target Groups
  • Lecturers and students of computer science, especially in optimization methods and renewable energies
  • Natural scientists interested in advanced heuristics
The Author
Dr. Daniel Lückehe defended his PhD thesis in the PhD program “System Integration of Renewable Energy” at the Carl von Ossietzky University in Oldenburg, Germany. As postdoctoral researcher he conducts research in computational health informatics at the Leibnitz University in Hanover, Germany.


Caracteristici

Study in Technical Sciences Includes supplementary material: sn.pub/extras