Evolutionary Wind Turbine Placement Optimization with Geographical Constraints

Gespeichert in:

Personen und Körperschaften:	Lückehe, Daniel (VerfasserIn)
Titel:	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints/ by Daniel Lückehe
Format:	E-Book
Sprache:	Englisch
veröffentlicht:	Wiesbaden Springer Vieweg 2017
Gesamtaufnahme:	SpringerLink Springer eBook Collection
Schlagwörter:	Computer science Computers Applied mathematics Engineering mathematics Sustainable development Computer Science
Quelle:	Verbunddaten SWB


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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
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contents	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, Solving Optimization Problems -- Wind Prediction Model -- Geographical Planning Scenarios -- Constrained Placement Optimization -- Constraint Handling with Penalty Functions -- Advanced Evolutionary Heuristics
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spelling	Lückehe, Daniel aut, Evolutionary Wind Turbine Placement Optimization with Geographical Constraints by Daniel Lückehe, Wiesbaden Springer Vieweg 2017, Online-Ressource (XXII, 195 p. 64 illus., 15 illus. in color, online resource), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, SpringerLink Bücher, Springer eBook Collection Computer Science, 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, Solving Optimization Problems -- Wind Prediction Model -- Geographical Planning Scenarios -- Constrained Placement Optimization -- Constraint Handling with Penalty Functions -- Advanced Evolutionary Heuristics, Computer science, Computers, Applied mathematics, Engineering mathematics, Sustainable development, Computer Science, s (DE-588)4128839-7 (DE-627)104724927 (DE-576)209605502 Windkraftwerk gnd, s (DE-588)4189962-3 (DE-627)105253197 (DE-576)210063475 Windturbine gnd, s (DE-588)4301515-3 (DE-627)121584593 (DE-576)211039616 Standortproblem gnd, s (DE-588)4024772-7 (DE-627)106296590 (DE-576)208957294 Heuristik gnd, s (DE-588)4366912-8 (DE-627)18175827X (DE-576)211690708 Evolutionärer Algorithmus gnd, s (DE-588)4429674-5 (DE-627)221360360 (DE-576)212363301 Geoinformation gnd, s (DE-588)4247482-6 (DE-627)104382228 (DE-576)210488131 Beschränkung gnd, s (DE-588)4437829-4 (DE-627)22347309X (DE-576)212444891 Penalty-Methode gnd, DE-101, 9783658184643, Druckausg. 978-3-658-18464-3, Printed edition 9783658184643, https://doi.org/10.1007/978-3-658-18465-0 B:SPRINGER Verlag lizenzpflichtig Volltext, https://swbplus.bsz-bw.de/bsz489629199cov.jpg V:DE-576 X:springer image/jpeg 20170614170631 Cover, (DE-627)889268193, http://dx.doi.org/10.1007/978-3-658-18465-0 DE-Ch1, DE-Ch1 epn:3407204787 2017-06-09T15:13:49Z, http://dx.doi.org/10.1007/978-3-658-18465-0 DE-Zwi2, DE-Zwi2 epn:3407204841 2017-06-09T15:13:49Z
spellingShingle	Lückehe, Daniel, 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 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, Solving Optimization Problems -- Wind Prediction Model -- Geographical Planning Scenarios -- Constrained Placement Optimization -- Constraint Handling with Penalty Functions -- Advanced Evolutionary Heuristics, Computer science, Computers, Applied mathematics, Engineering mathematics, Sustainable development, Computer Science, Windkraftwerk, Windturbine, Standortproblem, Heuristik, Evolutionärer Algorithmus, Geoinformation, Beschränkung, Penalty-Methode
swb_id_str	489629199
title	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints
title_auth	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints
title_full	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints by Daniel Lückehe
title_fullStr	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints by Daniel Lückehe
title_full_unstemmed	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints by Daniel Lückehe
title_short	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints
title_sort	evolutionary wind turbine placement optimization with geographical constraints
title_unstemmed	Evolutionary Wind Turbine Placement Optimization with Geographical Constraints
topic	Computer science, Computers, Applied mathematics, Engineering mathematics, Sustainable development, Computer Science, Windkraftwerk, Windturbine, Standortproblem, Heuristik, Evolutionärer Algorithmus, Geoinformation, Beschränkung, Penalty-Methode
topic_facet	Computer science, Computers, Applied mathematics, Engineering mathematics, Sustainable development, Computer Science, Windkraftwerk, Windturbine, Standortproblem, Heuristik, Evolutionärer Algorithmus, Geoinformation, Beschränkung, Penalty-Methode
url	https://doi.org/10.1007/978-3-658-18465-0, https://swbplus.bsz-bw.de/bsz489629199cov.jpg, http://dx.doi.org/10.1007/978-3-658-18465-0