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|a 9783658184650
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|a 10.1007/978-3-658-18465-0
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|a Lückehe, Daniel.
|e author.
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|4 http://id.loc.gov/vocabulary/relators/aut
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|a Evolutionary Wind Turbine Placement Optimization with Geographical Constraints
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|c by Daniel Lückehe.
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|a 1st ed. 2017.
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|a Wiesbaden :
|b Springer Fachmedien Wiesbaden :
|b Imprint: Springer Vieweg,
|c 2017.
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|a XXII, 195 p. 64 illus., 15 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|b PDF
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|a Solving Optimization Problems -- Wind Prediction Model -- Geographical Planning Scenarios -- Constrained Placement Optimization -- Constraint Handling with Penalty Functions -- Advanced Evolutionary Heuristics.
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|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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|a Applied mathematics.
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|a Engineering mathematics.
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|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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|a Sustainable Development.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/U34000
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|a Mathematical and Computational Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006
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|u https://doi.org/10.1007/978-3-658-18465-0
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|a Computer Science (R0) (SpringerNature-43710)
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