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Wind Speed Prediction Research Articles

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1651 Articles

Published in last 50 years

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  • Wind Speed Forecasting
  • Wind Speed Forecasting
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Articles published on Wind Speed Prediction

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Comparison of multilayer perceptron and nonlinear autoregressive models for wind speed prediction

Wind energy is a critical component of the global shift to renewable energy sources, with significant growth driven by the need to reduce carbon emissions. Accurate wind speed prediction is crucial for increasing wind energy output since it directly influences wind farm design and performance. The purpose of this study is to compare two artificial neural network (ANN) models for predicting wind speed in Dakhla City, a place with a high wind energy potential. The first model is a multilayer perceptron (MLP) trained with the backpropagation algorithm, while the second is a nonlinear autoregressive with exogenous inputs (NARX) model, a form of recurrent neural network (RNN) noted for its ability to handle time-series data more well. The comparative analysis results show that the NARX model outperforms the MLP model in terms of wind speed forecast accuracy. The NARX model achieved a near-perfect regression coefficient (R) of 0.9998 and a root mean square error (RMSE) of 1.02899, indicating that it can represent complex, nonlinear wind speed patterns. These findings indicate that the NARX model could be a beneficial tool for increasing the efficiency of Dakhla City’s wind energy infrastructure, assisting the region in meeting its renewable energy ambitions.

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  • Journal IconBulletin of Electrical Engineering and Informatics
  • Publication Date IconJun 1, 2025
  • Author Icon Houda Kacimi + 5
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Adaptive torque feed-forward control for wind turbine MPPT considering predicted wind speed characteristics

Adaptive torque feed-forward control for wind turbine MPPT considering predicted wind speed characteristics

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  • Journal IconInternational Journal of Electrical Power & Energy Systems
  • Publication Date IconJun 1, 2025
  • Author Icon Liangwen Qi + 6
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Enhanced Miss Forest and Multivariate Time Series Prediction of Wind Speed Using Deep Learning

As environmental concerns become increasingly serious, renewable energy sources are being prioritized to meet growing demands, among which wind energy stands out as a technologically efficient option. However, the unpredictable nature of offshore wind energy continues to pose challenges for reliable integration into power grids. This study tackles this problem by focusing on improving wind speed prediction by employing a modified Miss Forest algorithm for time series imputation, which achieved the lowest NRMSE: 0.0079, 0.0156 and 0.0730 among the other imputation methods when compared across three datasets. Alongside the Miss Forest, deep learning models such as Bayesian Optimized Stacked Long Short-Term Memory (BO-SLSTM), Deep Long Short-Term Memory (DLSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) and a hybrid Convolutional Long Short-Term Memory (CNN-LSTM) model were applied on real-time wind speed data from an offshore site in Gujarat, India. Furthermore, the Friedman Test was conducted to assess the statistical difference in model performance, yielding a p-value of 0.721, indicating no significant difference among the models. Among the models, the DLSTM demonstrated the best performance solely in terms of the error metrics, while CNN proved to be the most computationally efficient.

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  • Journal IconJournal of Circuits, Systems and Computers
  • Publication Date IconMay 8, 2025
  • Author Icon Tarran Sidhaarth + 4
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Short-term Wind Speed Prediction Model Based on Secondary Decomposition and SE-SSA-TCN

Short-term Wind Speed Prediction Model Based on Secondary Decomposition and SE-SSA-TCN

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  • Journal IconResearch Review
  • Publication Date IconMay 8, 2025
  • Author Icon Qi Guo
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Interpretable machine learning for coastal wind prediction: Integrating SHAP analysis and seasonal trends

Accurate wind speed prediction plays an important role in developing effective coastal management strategies and risk assessments, especially in coastal region managements to reduce erosion damage. In offshore wind energy, precise forecasts optimize wind farm layout and operations, maximizing energy yield and minimizing downtime. Additionally, accurate wind speed forecasts significantly improve maritime transportation safety by predicting hazardous conditions. Understanding wind patterns is also important for coastal ecosystem management and safer navigation activities. However, accurate wind speed prediction in dynamic coastal environments remains challenging due to (1) limited applications of robust machine learning (ML) models tailored to coastal meteorological complexity, (2) insufficient integration of interpretable feature analysis with predictive modeling for actionable insights, and (3) gaps in understanding how seasonal and diurnal wind patterns influence model performance in understudied regions like tropical Queensland. This study focuses on Abbot Point, Queensland, Australia, using meteorological data collected hourly from January 1 to December 31, 2023 (Latitude: -19.9496; Longitude: 148.0482). It evaluates three machine ML models—Linear Regression (LR), Decision Tree Regressor (DT), and Random Forest (RF)—to identify the most reliable approach for wind speed forecasting. The dataset includes wind direction, air temperature, relative humidity, precipitation, and barometric pressure as feature variables, with wind speed as the target variable. Novel integration of SHapley Additive exPlanations (SHAP) analysis and seasonal decomposition addresses interpretability gaps, while rigorous validation across training (70%), testing (15%), and validation (15%) datasets ensures model robustness. The RF model consistently outperformed others across training, validation, and test datasets, achieving the lowest mean square error (MSE: Train 0.183, Validation 0.875, Test 0.803), highest R2 (Train 0.966, Validation 0.831, Test 0.844), and superior Nash–Sutcliffe Efficiency (NSE: Train 0.96, Validation 0.83, Test 0.84). These results reflect the model's robust ability to capture complex relationships in the data. In contrast, LR and DT exhibited moderate accuracy, with higher MSE and lower NSE values, struggling particularly with consistency and extreme values. Complementary analyses, including wind rose plots and time series of wind speed, relative humidity, and barometric pressure, revealed high-risk periods characterized by strong winds (> 10 m/s), high humidity (> 90%), and low barometric pressure (< 1000 hPa). Seasonal analysis revealed spring/summer peaks in hazardous winds (> 10 m/s), with diurnal cycles (24-h periodicity) significantly influencing prediction accuracy—a pattern underemphasized in prior coastal ML studies. This study bridges critical gaps by demonstrating how interpretable ML enhances coastal wind prediction through: a) quantitative validation of RFR's superiority over traditional models in handling coastal meteorological variability, b) SHAP-driven identification of dominant predictors (wind direction, pressure) for targeted monitoring, c) Seasonal-temporal analysis framework for site-specific risk mitigation strategies. These findings confirm the interactions between meteorological variables that intensify storm risks and coastal hazards. Key insights include the dominant influence of southeast and south-southwest winds (100°–200°) and the critical role of barometric pressure in driving extreme wind events. Also, findings enable improved storm surge modeling and early warning systems by providing 6-h wind forecasts with 84% accuracy, directly informing coastal defense alignment with dominant wind-driven erosion patterns. This approach addresses the critical need for ML applications that combine predictive power with operational interpretability in coastal management contexts. The integration of ML models with detailed meteorological patterns supports the identification of high-risk periods, enabling targeted interventions such as strengthening coastal defenses and issuing early warnings. This study underscores the value of ML techniques, particularly RF, in enhancing predictive frameworks for coastal risk management and promoting sustainable, resilient coastal environments.

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  • Journal IconJournal of Coastal Conservation
  • Publication Date IconMay 6, 2025
  • Author Icon Ahmet Durap
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Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana, Egypt

Wind energy has become a key answer to the world’s energy problems, providing a clean and sustainable option instead of relying on fossil fuels. Enhancing wind energy systems and energy management is essential through efficient wind speed prediction. However, the complex nature of wind speed data contains significant challenges with existing forecasting models for long-term nonlinear forecasting accuracy, and this causes a lack of wind energy predictions, which may cause false distributions of energy. This study proposes a multi-step methodology that integrates Variational Mode Decomposition (VMD) with advanced machine learning like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbor (KNN), and transformer-based model (Informer) to improve long-term wind speed forecasting. The approach involves data collection from the NASA Power project, which consists of 35k samples of wind speed data, with performance evaluated on R-squared (R²) score and error metrics. The proposed approach demonstrated state-of-the-art performance, with LightGBM achieving the highest R² of 98% and the lowest error metrics. XGBoost and KNN performed slightly lower in R², achieving 97% score. Despite the high performance of the Informer model, it demonstrated the lowest in scores with a 78% R² score. The study’s novelty lies in highlighting the effectiveness and efficiency of VMD in addressing the complexities of wind speed data and underscores the potential of combining decomposition techniques with advanced machine learning models for accurate wind speed forecasting.

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  • Journal IconScientific Reports
  • Publication Date IconMay 4, 2025
  • Author Icon Ali Taha + 2
Open Access Icon Open Access
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Improving Offshore Wind Speed Forecasting with a CRGWAA-Enhanced Adaptive Neuro-Fuzzy Inference System

Accurate forecasting of offshore wind speed is crucial for the efficient operation and planning of wind energy systems. However, the inherently non-stationary and highly volatile nature of wind speed, coupled with the sensitivity of neural network-based models to parameter settings, poses significant challenges. To address these issues, this paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by CRGWAA. The proposed CRGWAA integrates Chebyshev mapping initialization, an elite-guided reflection refinement operator, and a generalized quadratic interpolation strategy to enhance population diversity, adaptive exploration, and local exploitation capabilities. The performance of CRGWAA is comprehensively evaluated on the CEC2022 benchmark function suite, where it demonstrates superior optimization accuracy, convergence speed, and robustness compared to six state-of-the-art algorithms. Furthermore, the ANFIS-CRGWAA model is applied to short-term offshore wind speed forecasting using real-world data from the offshore region of Fujian, China, at 10 m and 100 m above sea level. Experimental results show that the proposed model consistently outperforms conventional and hybrid baselines, achieving lower MAE, RMSE, and MAPE, as well as higher R2, across both altitudes. Specifically, compared to the original ANFIS-WAA model, the RMSE is reduced by approximately 45% at 10 m and 24% at 100 m. These findings confirm the effectiveness, stability, and generalization ability of the ANFIS-CRGWAA model for complex, non-stationary offshore wind speed prediction tasks.

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  • Journal IconJournal of Marine Science and Engineering
  • Publication Date IconMay 3, 2025
  • Author Icon Yingjie Liu + 1
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Machine Learning for Wind Speed Estimation

For more than two decades, computational analysis has been pivotal in expanding architectural capabilities, enabling sustainable design through detailed environmental analysis. Central to creating sustainable environments is the profound understanding of wind dynamics, which significantly influence comfort levels around buildings. Traditionally, wind tunnel experiments, in situ measurements, and computational fluid dynamics (CFD) simulations have been employed to assess wind speeds in urban settings. However, the advent of machine learning (ML) has introduced innovative methodologies that extend beyond these conventional approaches, offering new insights and applications in architectural design. This study focuses on evaluating pedestrian-level wind speeds using ML techniques, with a comparative analysis against traditional in situ measurements and CFD simulations. Our findings reveal that ML can predict wind speeds with sufficient accuracy for preliminary design phases. One of the primary challenges addressed is the integration of visual outputs from ML models with quantitative data, a necessary step to enhance model reliability and applicability. By developing novel techniques for this integration, our research marks a significant contribution to the field, benchmarking the effectiveness of ML against established methods. The results validate the ML model’s capability to accurately estimate wind speeds, thereby supporting the design of more sustainable and comfortable urban environments.

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  • Journal IconBuildings
  • Publication Date IconMay 2, 2025
  • Author Icon Ilker Karadag + 1
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A novel hybrid approach for hourly wind speed forecasting based on variational mode decomposition, data feature reconstruction, and machine learning

Short-term wind speed prediction plays a vital role in wind power generation, which has a significant impact on dispatching and operation decisions. However, the hourly wind speed time series exhibits the feature of high intermittency and high fluctuation, limiting the forecasting performance and timeliness. Therefore, the novel intelligent hybrid prediction approach was developed based on variational mode decomposition (VMD), fuzzy entropy test (FE), and Elman neural network. In the proposed approach, the chaos degree of wind speed time series was deceased by VMD to create a great environment for the following prediction work. Considering the timeliness, the reconstruction based on FE was designed, which reduced the number of times to run forecasting model. The proposed approach is applied to the five cases collected from two different wind farms. The obtained results indicated that the proposed approach owned the optimal performance and its average prediction accuracy was improved by 56.40% compared with that of other comparative models. Meanwhile, the timeliness of the proposed approach was doubled after the reconstruction based on FE. Hence, the proposed approach can meet the requirements of wind farm to obtain the accurate prediction of hourly wind speed timely.

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  • Journal IconJournal of Renewable and Sustainable Energy
  • Publication Date IconMay 1, 2025
  • Author Icon Shunyu Zhao + 5
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Wavelet-denoised graph-Informer for accurate and stable wind speed prediction

Wavelet-denoised graph-Informer for accurate and stable wind speed prediction

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  • Journal IconApplied Soft Computing
  • Publication Date IconMay 1, 2025
  • Author Icon Biao Yu + 2
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A point-interval wind speed prediction model based on entropy clustering and hybrid optimization weighted strategy

A point-interval wind speed prediction model based on entropy clustering and hybrid optimization weighted strategy

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  • Journal IconRenewable Energy
  • Publication Date IconMay 1, 2025
  • Author Icon Jujie Wang + 2
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Evaluation of Artificial Neural Networks (ANNs) and Multivariate Adaptive Regression Splines (MARS) for Monthly Mean Land Surface Temperature (LST) Modelling– A Case Study of Aowin District, The Republic of Ghana

Accurate and precise estimations of Land Surface Temperature (LST) are essential in climatology, agribusiness, agronomy, urban planning, aviation, and hydrology studies. In this study, the feasibility of two soft computing methods, thus; fifteen different Artificial Neural Network (ANN) architectures and the data mining model of Multivariate Adaptive Regression Splines (MARS) is evaluated for predicting the monthly mean LST of Aowin District, Ghana. Various weather prediction variables, including precipitation, relative humidity, wind speed, and temperature time series historical data spanning 37 years (from 1st January 1985 to 31st December 2022), were used. The data was obtained from a satellite database repository and used in the ANN and MARS models' formulation as input (independent variables) and output (dependent variable), respectively. Five different statistical performance indicators, namely mean error (ME), root mean absolute error (RMAE), mean squared error (MSE), root mean squared error (RMSE), and standard deviation (SD), were used to assess the accuracy and precision of LST estimates from both the ANN and MARS models for the research area. The results demonstrate the capability of both techniques in predicting the monthly mean LST. However, the MARS model produced the best LST estimate, with statistical metrics of ME, RMAE, MSE, RMSE, and SD being 1.8705E-07 °C, 0.0004 °C, 3.3449 °C, 5.7835 °C, and 1.6000E-09 °C, respectively. Both ANN and MARS methods can be effectively applied for LST estimation in the research region and for studying the potential impacts of climate change dynamics globally.

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  • Journal IconJournal of Geomatics
  • Publication Date IconApr 30, 2025
  • Author Icon Michael Stanley Peprah + 3
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Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices

Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices

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  • Journal IconEnergies
  • Publication Date IconApr 23, 2025
  • Author Icon Laeeq Aslam + 6
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Machine Learning for Sustainable Forecasting: Adaptive Wind Speed Prediction Using Functional Data

The global switch to sustainable and clean electricity sources depends heavily on wind energy. To ensure grid stability, minimise operating costs, and optimise the efficiency of wind energy systems, accurate wind speed forecasts is crucial. Using functional data from past weather patterns, this study proposes an adaptive machine learning-based method for wind speed prediction. Time-based indicators, temperature, humidity, atmospheric pressure, dew point, and other important meteorological characteristics are included in the dataset, which was gathered via the Open-Meteo weather API for the years 2024–2025. Advanced preprocessing methods, including feature scaling, correlation analysis, and outlier treatment, along with thorough exploratory data analysis, greatly enhanced the quality of the data and the performance of the model. Standard performance metrics including MAE, MSE, RMSE, and R2 score were used to train and assess a variety of regression models, such as Linear Regression, Random Forest, XGBoost, and LightGBM. When it came to capturing the non-linear patterns of wind speed, ensemble-based models performed better. The results highlight the potential of machine learning models in creating reliable, real-time forecasting systems for sustainable energy planning and validate their efficacy within a functional data horizon. Keywords: Wind Speed Forecasting, Sustainable Forecasting, Ensemble Models XGBoost, Weather Prediction, Open-Meteo API, Regression Model.

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  • Journal IconInternational Scientific Journal of Engineering and Management
  • Publication Date IconApr 20, 2025
  • Author Icon Rupadevi Rupadevi
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A machine learning model for hub-height short-term wind speed prediction

Accurate short-term wind speed prediction is crucial for maintaining the safe, stable, and efficient operation of wind power systems. We propose a multivariate meteorological data fusion wind prediction network (MFWPN) to study fine-grid vector wind speed prediction, taking Northeast China as an example. Results show that MFWPN outperforms the ECMWF-HRES model regarding vector wind speed prediction accuracy within the first 6 h. Transfer experiments demonstrate the good generalized performance of the MFWPN, which can be quickly applied to offsite prediction. Efficiency experiments show that the MFWPN takes only 18 ms to predict vector wind speeds on a 24-hour fine grid over the future northeastern region. With its demonstrated accuracy and efficiency, the MFWPN can be an effective tool for predicting vector wind speeds in large regional wind centers and can help in ultrashort- and short-term deployment planning for wind power.

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  • Journal IconNature Communications
  • Publication Date IconApr 3, 2025
  • Author Icon Zongwei Zhang + 5
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Multivariate short-term wind speed prediction based on CNN-LSTM-SA models

Abstract This paper investigates the application of the CNN-LSTM-SA model for wind speed prediction. It is found that compared with the univariate input method which only inputs the historical wind speed, the multivariate input prediction model can substantially improve the prediction accuracy. In the experiments of this paper, out of the CNN model, other multi-input prediction models improve the prediction accuracy by about 40% compared to the single-input model. In addition, this paper experimentally investigates the effect of the attention mechanism on the multi-input wind speed prediction model: the attention mechanism improves the CNN model significantly, the prediction accuracy improves by about 53%, and the degree of overfitting decreases dramatically; the effect on the LSTM model is completely negative; the improvement of the prediction accuracy of the CNN-LSTM model is not significant, and the degree of its overfitting decreases by about 66.6%.

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  • Journal IconJournal of Physics: Conference Series
  • Publication Date IconApr 1, 2025
  • Author Icon Ke Ding + 1
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A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction

A multi-scale component feature learning framework based on CNN-BiGRU and online sequential regularized extreme learning machine for wind speed prediction

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  • Journal IconRenewable Energy
  • Publication Date IconApr 1, 2025
  • Author Icon Xuedong Zhang + 6
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A novel wind speed prediction model based on neural networks, wavelet transformation, mutual information, and coot optimization algorithm

Wind is a renewable, sustainable, and clean source of energy. This has led to wind gaining a lot of attention in recent decades as a reliable alternative to fossil fuels. However, wind speed fluctuations complicate its integration with power grids. To tackle this issue, this paper proposes a new wind speed prediction model that combines four techniques: Discrete Wavelet Transform, which smooths the wind speed signal; Mutual Information, which selects the most informative part of the wind speed time series; Coot Optimization Algorithm for optimal feature selection; and Bidirectional Long Short-Term Memory for capturing complex patterns. To evaluate the efficiency of the proposed model, its performance was measured using error metrics such as mean squared error, mean absolute error, mean absolute percentage error, coefficient of determination (), and median absolute error. The proposed model was examined using two different wind speed datasets and achieved high prediction accuracy. Additionally, 14 different benchmark models were created, and their prediction results were compared with those of the proposed model. A comparison between the results of the proposed model and benchmark models demonstrated the superiority of the proposed model.

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  • Journal IconScientific Reports
  • Publication Date IconMar 29, 2025
  • Author Icon Faezeh Amirteimoury + 4
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A fusion approach of discrete wavelet decomposition and deep learning techniques for the enhancement of wind speed prediction accuracy

A fusion approach of discrete wavelet decomposition and deep learning techniques for the enhancement of wind speed prediction accuracy

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  • Journal IconTheoretical and Applied Climatology
  • Publication Date IconMar 29, 2025
  • Author Icon Sarvendra Kumar Singh + 2
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Evaluation of Six Subgrid-Scale Models for LES of Wind Farms in Stable and Conventionally-Neutral Atmospheric Stratification

The performance of six subgrid-scale (SGS) models is analyzed for large-eddy simulations (LES) of wind-farm flows under stable (SBL) and conventionally-neutral (CNBL) atmospheric conditions. A precursor–concurrent technique is employed to provide fully developed turbulent inflow for simulations of a 40-turbine wind farm. Turbines are represented using the actuator-disc method, employing a baseline grid of 12 cells across the turbine diameter. The SBL precursor flow poses a challenge for LES, as it may not be able to resolve the small turbulent scales featured in this flow if the grid is coarse. For these precursor flows, the baseline grid results of all six SGS models are assessed relative to coarser and finer grids, with 6 and 45 cells across the diameter, respectively. The wall-adapting local eddy-viscosity (WALE) and Lagrangian-averaged scale-dependent dynamic (LASDD) models exhibit high grid sensitivity, while the standard Smagorinsky (Smag.), anisotropic minimum-dissipation (AMD), one-equation turbulent kinetic energy (TKE), and stability-dependent Smagorinsky (SDS) models show low sensitivity. For the wind-farm simulations conducted with the baseline grid, the AMD and SDS models predict similar wind-farm performance. In contrast, the WALE and LASDD models predict nearly 30% less power output, primarily due to their prediction of lower inflow wind speeds. CNBL simulations on the baseline grid show reduced sensitivity to the SGS model due to larger atmospheric turbulence and length scales compared to the SBL flow. Among the six models, the AMD model demonstrates ease of implementation, the least sensitivity to grid size for the SBL precursor flow, and predictions that are consistent with other models and higher-order pseudo-spectral LES solvers, making it a suitable choice for LES of wind-farm flows under both stable and conventionally-neutral conditions.

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  • Journal IconBoundary-Layer Meteorology
  • Publication Date IconMar 25, 2025
  • Author Icon Mina Ghobrial + 3
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