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Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Survey of modelling methods for wind turbine wakes and wind farms
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Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm
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Wind plant system engineering through optimization of layout and yaw control
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Combining economic and fluid dynamic models to determine the optimal spacing in very large wind farms
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Model‐based receding horizon control of wind farms for secondary frequency regulation
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Modeling space‐time correlations of velocity fluctuations in wind farms
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Measurements behind model wind turbines: further evidence of wake meandering
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Modelling and measuring flow and wind turbine wakes in large wind farms offshore
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Optimal turbine spacing in fully developed wind farm boundary layers
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Modeling wake effects in large wind farms in complex terrain: the problem, the methods and the issues
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Mesoscale to microscale wind farm flow modeling and evaluation
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Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel
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Effects of Thermal Stability and Incoming Boundary-Layer Flow Characteristics on Wind-Turbine Wakes: A Wind-Tunnel Study
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A data-driven, cooperative wind farm control to maximize the total power production
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Large-eddy simulation of a utility-scale wind farm in complex terrain
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Optimization of wind farm turbines layout using an evolutive algorithm
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A new analytical model for wind-turbine wakes
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A review on the forecasting of wind speed and generated power
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Wind turbine wake models developed at the technical university of Denmark: A review
inferredcites →https://doi.org/10.1017/jfm.2016.615openalex-snapshot
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
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Deep learning in fluid dynamics
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Implication of Taylor’s hypothesis on measuring flow modulation
inferredcites →https://doi.org/10.1017/jfm.2018.554openalex-snapshot
Dependence of small-scale energetics on large scales in turbulent flows
inferredcites →https://doi.org/10.1017/jfm.2018.731openalex-snapshot
The anatomy of large-scale motion in atmospheric boundary layers
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Modelling yawed wind turbine wakes: a lifting line approach
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Evidence of very long meandering features in the logarithmic region of turbulent boundary layers
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Deep learning
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Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development
inferredcites →https://doi.org/10.1038/s41598-018-20389-yopenalex-snapshot
First in situ evidence of wakes in the far field behind offshore wind farms
inferredcites →https://doi.org/10.1063/1.4915287openalex-snapshot
Coupled wake boundary layer model of wind-farms
inferredcites →https://doi.org/10.1073/pnas.1903680116openalex-snapshot
Wind farm power optimization through wake steering
inferredcites →https://doi.org/10.1080/14685248.2019.1584664openalex-snapshot
Big wind power: seven questions for turbulence research
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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm
inferredcites →https://doi.org/10.1088/1742-6596/1037/7/072003openalex-snapshot
Influence of the horizontal component of Earth’s rotation on wind turbine wakes
inferredcites →https://doi.org/10.1088/1742-6596/1037/7/072008openalex-snapshot
Wake behind an offshore wind farm observed with dual-Doppler radars
inferredcites →https://doi.org/10.1088/1742-6596/524/1/012186openalex-snapshot
A Control-Oriented Dynamic Model for Wakes in Wind Plants
inferredcites →https://doi.org/10.1088/1742-6596/625/1/012008openalex-snapshot
Simulation of wake effects between two wind farms
inferredcites →https://doi.org/10.1088/1742-6596/625/1/012026openalex-snapshot
Predicting wind farm wake interaction with RANS: an investigation of the Coriolis force
inferredcites →https://doi.org/10.1088/1742-6596/625/1/012039openalex-snapshot
A new analytical model for wind farm power prediction
inferredcites →https://doi.org/10.1103/physrevlett.110.138701openalex-snapshot
Turbulent Character of Wind Energy
inferredcites →https://doi.org/10.1109/2945.841119openalex-snapshot
Graph visualization and navigation in information visualization: A survey
inferredcites →https://doi.org/10.1109/ijcnn.1990.137819openalex-snapshot
Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
inferredcites →https://doi.org/10.1109/tcst.2015.2508007openalex-snapshot
Bayesian Ascent: A Data-Driven Optimization Scheme for Real-Time Control With Application to Wind Farm Power Maximization
inferredcites →https://doi.org/10.1109/tpwrs.2006.889078openalex-snapshot
An Advanced Statistical Method for Wind Power Forecasting
inferredcites →https://doi.org/10.1109/tpwrs.2013.2287871openalex-snapshot
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
inferredcites →https://doi.org/10.1109/tste.2011.2182215openalex-snapshot
AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network
inferredcites →https://doi.org/10.1109/tste.2012.2204069openalex-snapshot
Coordinating Large-Scale Wind Integration and Transmission Planning
inferredcites →https://doi.org/10.1126/science.1188765openalex-snapshot
Predictive Model for Wall-Bounded Turbulent Flow
inferredcites →https://doi.org/10.1146/annurev-fluid-010816-060206openalex-snapshot
Flow Structure and Turbulence in Wind Farms
inferredcites →https://doi.org/10.1146/annurev.fluid.30.1.539openalex-snapshot
DIRECT NUMERICAL SIMULATION: A Tool in Turbulence Research
inferredcites →https://doi.org/10.1257/jep.15.4.143openalex-snapshot
Quantile Regression
inferredcites →https://doi.org/10.2172/1351062openalex-snapshot
2015 Cost of Wind Energy Review
inferredcites →https://doi.org/10.23919/acc.2017.7962923openalex-snapshot
A tutorial on control-oriented modeling and control of wind farms
inferredcites →https://doi.org/10.23919/acc.2018.8430751openalex-snapshot
Efficient Optimization of Large Wind Farms for Real-Time Control
inferredcites →https://doi.org/10.23919/acc.2018.8431764openalex-snapshot
Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication
inferredcites →https://doi.org/10.2514/6.2019-0538openalex-snapshot
Comparison of Wind Farm Layout Optimization Results Using a Simple Wake Model and Gradient-Based Optimization to Large Eddy Simulations
inferredcites →https://doi.org/10.5194/wes-2-229-2017openalex-snapshot
Field test of wake steering at an offshore wind farm
inferredcites →https://doi.org/10.5194/wes-3-749-2018openalex-snapshot
Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control
inferredcites →https://doi.org/10.5194/wes-3-819-2018openalex-snapshot
Analysis of control-oriented wake modeling tools using lidar field results
inferredcites →https://doi.org/10.5194/wes-4-127-2019openalex-snapshot
The aerodynamics of the curled wake: a simplified model in view of flow control
inferredcites →https://doi.org/10.5194/wes-4-273-2019openalex-snapshot
Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1
inferredcites →https://doi.org/10.5194/wes-4-355-2019openalex-snapshot
Wind direction estimation using SCADA data with consensus-based optimization
inferredcites →https://doi.org/10.7551/mitpress/10187.001.0001openalex-snapshot
Decision Making Under Uncertainty

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A Condition Monitoring Approach Based on Multiple Linear Models for Fault Detection in Industrial-Scale Wind Farms
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Maximizing Insights from Wind Turbine Data Through Data-Driven Approaches
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Dynamic wake modulation induced by utility-scale wind turbine operation
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Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network
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Physics-informed spatio-temporal network with trainable adaptive feature selection for short-term wind speed prediction
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Effect of physics regularization on generative adversarial training for fast prediction of yawed wind turbine wakes
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Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight
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A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design
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Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114301openalex-snapshot
Spatiotemporal physics-guided graph neural network for wind farm power prediction
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Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake
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A meteorology-free wind power forecasting using a hybrid physics-aware, SCADA-only ensemble residual learning
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A call for enhanced data-driven insights into wind energy flow physics
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Wind farm yaw control set-point optimization under model parameter uncertainty
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