research-article

On learning to generate wind farm layouts

Authors:

Sylvain Cussat-Blanc,

Kalyan Veeramachaneni,

Una-May O'ReillyAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 767 - 774

https://doi.org/10.1145/2463372.2463462

Published: 06 July 2013 Publication History

Abstract

Optimizing a wind farm layout is a very complex problem that involves many local and global constraints such as inter-turbine wind interference or terrain peculiarities. Existing methods are either inefficient or, when efficient, take days or weeks to execute. Solutions are contextually sensitive to the specific values of the problem variables; when one value is modified, the algorithm has to be re-run from scratch. This paper proposes the use of a developmental model to generate farm layouts. Controlled by a gene regulatory network, virtual cells have to populate a simulated environment that represents the wind farm. When the cells' behavior is learned, this approach has the advantage that it is re-usable in different contexts; the same initial cell is responsive to a variety of environments and the layout generation takes few minutes instead of days.

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Cited By

Senderovich AZhang JCohen EBeck J(2022)Exploiting Hardware and Software Advances for Quadratic Models of Wind Farm Layout OptimizationIEEE Access10.1109/ACCESS.2022.319314310(78044-78055)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3193143
Ryerkerk MAverill RDeb KGoodman E(2019)A survey of evolutionary algorithms using metameric representationsGenetic Programming and Evolvable Machines10.1007/s10710-019-09356-2Online publication date: 17-Jun-2019
https://doi.org/10.1007/s10710-019-09356-2
Disset JWilson DCussat-Blanc SSanchez SLuga HDuthen YBosman P(2017)A comparison of genetic regulatory network dynamics and encodingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071322(91-98)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071322
Show More Cited By

Index Terms

On learning to generate wind farm layouts
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling

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Published In

cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Senderovich AZhang JCohen EBeck J(2022)Exploiting Hardware and Software Advances for Quadratic Models of Wind Farm Layout OptimizationIEEE Access10.1109/ACCESS.2022.319314310(78044-78055)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3193143
Ryerkerk MAverill RDeb KGoodman E(2019)A survey of evolutionary algorithms using metameric representationsGenetic Programming and Evolvable Machines10.1007/s10710-019-09356-2Online publication date: 17-Jun-2019
https://doi.org/10.1007/s10710-019-09356-2
Disset JWilson DCussat-Blanc SSanchez SLuga HDuthen YBosman P(2017)A comparison of genetic regulatory network dynamics and encodingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071322(91-98)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071322
Rodrigues SBauer PBosman P(2016)Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalabilityRenewable and Sustainable Energy Reviews10.1016/j.rser.2016.07.02165(587-609)Online publication date: Nov-2016
https://doi.org/10.1016/j.rser.2016.07.021
Cussat-Blanc SDisset JSanchez S(2016)Dangerousness Metric for Gene Regulated Car DrivingApplications of Evolutionary Computation10.1007/978-3-319-31204-0_40(620-635)Online publication date: 15-Mar-2016
https://doi.org/10.1007/978-3-319-31204-0_40
Vanaret CDurand NAlliot JArnold D(2014)Windmill farm pattern optimization using evolutionary algorithmsProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2598506(181-182)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2598506
Wilson DCussat-Blanc SVeeramachaneni KO'Reilly ULuga HArnold D(2014)A continuous developmental model for wind farm layout optimizationProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598383(745-752)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598383

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