Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1235-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-1235-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2023
Research article |
| 01 Aug 2023
| 01 Aug 2023
Stochastic gradient descent for wind farm optimization
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Pierre-Elouan Rethore
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mads Mølgaard Pedersen
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Rafael Valotta Rodrigues
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mikkel Friis-Møller
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Cited
15 citations as recorded by crossref.
- A knowledge-based optimization design method for supertall buildings with a strong outer frame structural system L. Han et al. 10.1016/j.jobe.2025.112803
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. 10.1063/5.0237778
- Parametric Analysis of Inter-Farm Wake Interactions in Offshore Wind Farm Projects Along the US East Coast A. Moura & R. Rodrigues 10.1088/1742-6596/3016/1/012049
- Need For Speed: Fast Wind Farm Optimization M. Sarcos et al. 10.1088/1742-6596/2767/9/092088
- Wind Farm Layout Optimization Accounting for Uncertainty in Model Selection N. O’Neill et al. 10.1088/1742-6596/3016/1/012054
- Gradient Descent Algorithm with Greedy Repositioning Using Power Deficit Aggregation of Wakes to Accelerate the Offshore Wind Farm Layout Optimization Problem in Irregular Concession Areas A. Gonzalez-Rodriguez et al. 10.3390/app142311331
- Practical trade-offs in neural network optimization: brute force search and gradient descent T. Somefun et al. 10.1088/2631-8695/adc5de
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. 10.5194/wes-9-321-2024
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Multidisciplinary Wind Farm Electrical Infrastructure Optimization R. Rodrigues & J. Rua 10.1088/1742-6596/2767/8/082021
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. 10.1088/1742-6596/3016/1/012024
- Gradient-based wind farm layout optimization with inclusion and exclusion zones J. Criado Risco et al. 10.5194/wes-9-585-2024
- Benchmarking Engineering Wake Models for Assessing Wind Farm Wakes Interaction R. Rodrigues & A. Moura 10.1088/1742-6596/3016/1/012045
- FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization M. LoCascio et al. 10.1002/we.2954
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
14 citations as recorded by crossref.
- A knowledge-based optimization design method for supertall buildings with a strong outer frame structural system L. Han et al. 10.1016/j.jobe.2025.112803
- Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients M. LoCascio et al. 10.1063/5.0237778
- Parametric Analysis of Inter-Farm Wake Interactions in Offshore Wind Farm Projects Along the US East Coast A. Moura & R. Rodrigues 10.1088/1742-6596/3016/1/012049
- Need For Speed: Fast Wind Farm Optimization M. Sarcos et al. 10.1088/1742-6596/2767/9/092088
- Wind Farm Layout Optimization Accounting for Uncertainty in Model Selection N. O’Neill et al. 10.1088/1742-6596/3016/1/012054
- Gradient Descent Algorithm with Greedy Repositioning Using Power Deficit Aggregation of Wakes to Accelerate the Offshore Wind Farm Layout Optimization Problem in Irregular Concession Areas A. Gonzalez-Rodriguez et al. 10.3390/app142311331
- Practical trade-offs in neural network optimization: brute force search and gradient descent T. Somefun et al. 10.1088/2631-8695/adc5de
- Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout R. Valotta Rodrigues et al. 10.5194/wes-9-321-2024
- Stochastic gradient descent for wind farm optimization J. Quick et al. 10.5194/wes-8-1235-2023
- Multidisciplinary Wind Farm Electrical Infrastructure Optimization R. Rodrigues & J. Rua 10.1088/1742-6596/2767/8/082021
- Revenue-Focused Wind Farm Control Co-Design for Future Electricity Markets Scenarios D. Dirik et al. 10.1088/1742-6596/3016/1/012024
- Gradient-based wind farm layout optimization with inclusion and exclusion zones J. Criado Risco et al. 10.5194/wes-9-585-2024
- Benchmarking Engineering Wake Models for Assessing Wind Farm Wakes Interaction R. Rodrigues & A. Moura 10.1088/1742-6596/3016/1/012045
- FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization M. LoCascio et al. 10.1002/we.2954
1 citations as recorded by crossref.
Latest update: 28 Jun 2025
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Wind turbine positions are often optimized to avoid wake losses. These losses depend on...
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