Evolutionary Wind Turbine Placement Optimization with Geographical Constraints

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 situatio...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Lückehe, Daniel (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2017.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Lückehe, Daniel.  |e author. 
245 1 0 |a Evolutionary Wind Turbine Placement Optimization with Geographical Constraints  |h [electronic resource] /  |c by Daniel Lückehe. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Vieweg,  |c 2017. 
300 |a XXII, 195 p. 64 illus., 15 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Solving Optimization Problems -- Wind Prediction Model -- Geographical Planning Scenarios -- Constrained Placement Optimization -- Constraint Handling with Penalty Functions -- Advanced Evolutionary Heuristics. 
520 |a 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. 
650 0 |a Computer science. 
650 0 |a Computers. 
650 0 |a Applied mathematics. 
650 0 |a Engineering mathematics. 
650 0 |a Sustainable development. 
650 1 4 |a Computer Science. 
650 2 4 |a Computing Methodologies. 
650 2 4 |a Sustainable Development. 
650 2 4 |a Appl.Mathematics/Computational Methods of Engineering. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783658184643 
856 4 0 |u http://dx.doi.org/10.1007/978-3-658-18465-0  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)