Physics-informed deep convolutional hierarchical encoder-decoder neural network for flow field prediction in wind farms

Physics-informed deep convolutional hierarchical encoder-decoder neural network for flow field prediction in wind farms

Wind Farm Layout Optimization (WFLO) is a critical step in wind farm design, focusing on determining the optimal placement of turbines to maximize the annual energy production (AEP) of wind farms. Calculating AEP as an objective function in WFLO often relies on computationally expensive computationa...

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Bibliographic Details
Main Authors: Saeede Hasanpoor, David A. Romero, Joaquin E. Moran, Cristina H. Amon
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Energy and AI
Subjects:
Surrogate models
Physics-informed convolutional encoder-decoder networks
Wind farm layout optimization
Annual energy production
Wind turbines
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546825000850
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http://www.sciencedirect.com/science/article/pii/S2666546825000850

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