Numerical Weather Prediction Wind Speed Research Articles

Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the NRMSE by 3.82 % and NMAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction

Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction

Research on Numerical Weather Prediction Wind Speed Correction Based on Stacking Ensemble Learning Algorithm

The transportation of wind fields plays a crucial role in the dispersion and distribution of air pollutants, and accurate wind speed prediction is essential for assessing pollutant concentrations. In this study, we constructed a Stacking ensemble learning model using three models, namely Random Forest, LightGBM, and XGBoost, as base learners, and the LASSO regression model as the meta-learner to optimize wind speed data forecasting for Zhengzhou City’s urban area using WRF. Firstly, based on Pearson correlation coefficients, we selected meteorological variables that have a significant impact on near-surface wind speed and derived historical lagged features. Bayesian TPE was utilized for hyperparameter tuning and model building. Finally, the performance of the trained model was evaluated by comparing it with ground observation data. The results showed that compared to the WRF model, Random Forest, LightGBM, and XGBoost effectively reduced forecast errors and significantly improved wind speed predictions. Both LightGBM and XGBoost demonstrated similar performance in the correction models across the 11 stations and outperformed Random Forest. The Stacking method integrated the advantages of the base learners and exhibited improvement capabilities over individual models, highlighting the potential of machine learning in enhancing localized and accurate weather forecasting.

A Hybrid Model for Long-Term Wind Power Forecasting Utilizing NWP Subsequence Correction and Multi-Scale Deep Learning Regression Methods

The accuracy of long-term wind power forecasting (WPF) is crucial for the efficient operation of grid systems. However, wind power generation is highly stochastic and intermittent due to the influence of weather, which makes long-term WPF less effective. Numerical weather prediction (NWP) data contains valuable weather forecast information, which can mitigate the negative effects of stochastic weather fluctuations on WPF. However, the accuracy of NWP data decreases over time, and multiple NWP data can have redundancy and errors that make it challenging to extract valid information. Reducing the variety and errors in NWP data and using more effective information extraction methods are essential for improving long-term WPF performance. In this article, we propose a novel long-term WPF hybrid model that corrects NWP wind speed and uses multi-scale deep learning regression prediction to exclude excessive NWP data. We use only the corrected NWP wind speed data to establish a nonlinear mapping relationship with actual power data. The validation case study shows that our proposed model reduces the mean squared error (MSE) and mean absolute error (MAE) by 65.0% and 43.8%, respectively, compared to the current state-of-the-art time series forecasting model in a seven-day forecasting scenario.

Considering dynamic perception of fluctuation trend for long-foresight-term wind power prediction

With the increasing proportion of renewable energy being integrated into the power system, the stable operation of the power system is facing great challenges. Breaking through the limitation and prolonging the time period of power prediction will help to improve the consumption ability of wind power (WP), adjust the power generation plan, and ensure the safe operation of the power system. Based on the availability of long-foresight-term numerical weather prediction (NWP) and fluctuation trend mining, a wind farm cluster power forecasting method based on dynamic perception of fluctuation trend is proposed for the long-foresight-term wind power prediction (WPP). The method first matches historical power based on the fluctuation trend of NWP wind speed (WS), and then obtains statistical characteristics of similar processes in order to construct new input features. Secondly, after the prediction results are obtained, the predicted power is corrected according to the fluctuation trend to improve the prediction reliability. This method is applied to a wind farm cluster in Gansu Province, China, and the overall prediction accuracy is 2.23 % higher than that of the direct prediction method. It is verified that this method can achieve long-foresight-term prediction and increase the reliability of prediction.

A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)

Abstract. Accurate wind speed prediction is crucial for the safe and efficient utilization of wind resources. However, current single-value deterministic numerical weather prediction methods employed by wind farms do not adequately meet the actual needs of power grid dispatching. In this study, we propose a new hybrid forecasting method for correcting 10 m wind speed predictions made by the Weather Research and Forecasting (WRF) model. Our approach incorporates variational mode decomposition (VMD), principal component analysis (PCA), and five artificial intelligence algorithms: deep belief network (DBN), multilayer perceptron (MLP), random forest (RF), eXtreme gradient boosting (XGBoost), light gradient boosting machine (lightGBM), and the Bayesian optimization algorithm (BOA). We first predict wind speeds using the WRF model, with initial and lateral boundary conditions from the Global Forecast System (GFS). We then perform two sets of experiments with different input factors and apply BOA optimization to tune the four artificial intelligence models, ultimately building the final models. Furthermore, we compare the aforementioned five optimal artificial intelligence models suitable for five provinces in southern China in the wintertime: VMD-PCA-RF in December 2021 and VMD-PCA-lightGBM in January 2022. We find that the VMD-PCA-RF evaluation indices exhibit relative stability over nearly a year: the correlation coefficient (R) is above 0.6, forecasting accuracy (FA) is above 85 %, mean absolute error (MAE) is below 0.6 m s−1, root mean square error (RMSE) is below 0.8 m s−1, relative mean absolute error (rMAE) is below 60 %, and relative root mean square error (rRMSE) is below 75 %. Thus, for its promising performance and excellent year-round robustness, we recommend adopting the proposed VMD-PCA-RF method for improved wind speed prediction in models.

Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. The quality of Numerical weather prediction (NWP) has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it’ s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and division of transitional weather process. The combined prediction method was first used to correct the NWP wind speed, and then we use the double clustering method to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSE is 5.93% and in MAE is 4.82% compared to the various comparison methods in the four seasons. The USTWPP method combining wind speed correction and double clustering division of transitional weather scenarios can significantly improve accuracy of USTWPP.

A hybrid method for numerical weather prediction wind speed based on Bayesian optimization (version 1.2.0) and error correction: First release of my code

A hybrid method for numerical weather prediction wind speed based on Bayesian optimization (version 1.2.0) and error correction: First release of my code

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

A novel pure data-selection framework for day-ahead wind power forecasting

Numerical weather prediction (NWP) data possess internal inaccuracies, such as low NWP wind speed corresponding to high actual wind power generation. This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework (PDF) to choose useful data prior to modeling, thus improving the accuracy of day-ahead wind power forecasting. Briefly, we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model. Although a small subset can increase selection flexibility, it can also produce billions of subset combinations, resulting in computational issues. To address this problem, we incorporated metamodeling and optimization steps into PDF. We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm, respectively.Experimental results demonstrate that (1) it is necessary to select data before constructing a forecasting model; (2) using a smaller subset will likely increase selection flexibility, leading to a more accurate forecasting model; (3) PDF can generate a better training dataset than similarity-based data selection methods (e.g., K-means and support vector classification); and (4) choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

Coupling High-Resolution Numerical Weather Prediction and Computational Fluid Dynamics: Auckland Harbour Case Study

In this study, the resilience of large cities and their built infrastructure in New Zealand to extreme winds, is investigated by coupling the outputs of a very high-resolution, 333-m resolution, numerical weather prediction (NWP) model with computational fluid dynamics (CFD) simulations. Following an extreme wind event on 18 September 2020 in Auckland, in which two trucks travelling over the Auckland Harbour bridge tipped over and damaged the bridge structure, a CFD simulation of airflow over the bridge using the Reynolds-averaged Navier–Stokes (RANS) method and NWP wind speed forecasts as the inlet profile is conducted. The 333 m NWP forecasts were validated against four nearby observation sites, showing generally high correlations of greater than 0.8 and low mean bias (±3 m s−1) and RMSE (<3 m s−1) values. The CFD-based estimates of the mean wind speed-up over the bridge showed that the mean wind speed could increase by a factor of 1.15–1.20 in the vicinity of the road where the toppled vehicles were travelling. Additionally, NWP forecasts and CFD estimates of wind gusts at the maximum bridge height, within the area not affected by the bridge structure, agreed well with a value of about 25 m s−1. An anemometer mounted at the top of the bridge arch measured a higher gust wind speed of about 35 m s−1 that could have been influenced by the gust speed-up resulting from the flow separation from the bridge arch, which is demonstrated in the CFD results. The results demonstrate the importance of understanding localised wind speed-up effects and distinguishing them from the approaching undisturbed airflow.

Day‐ahead wind power combination forecasting based on corrected numerical weather prediction and entropy method

To satisfy the grid operation scheduling requirements for wind power forecasting model accuracy, the measured wind speed near the height of the wind turbine hub is added to the wind power combined forecasting model. First, the relationship between the numerical weather prediction wind speed and the measured wind speed at different heights are analysed, and the correlation between each wind speed and the wind power is compared. Second, the random forest algorithm combined with the cumulative contribution rate is used to select several meteorological types of numerical weather prediction data as the input of the long short-term memory network to predict wind speed. Third, while inputting the meteorological data provided by numerical weather prediction, which is highly related to wind power, the wind power prediction network also uses the predicted wind speed of the upper network as input to predict wind power. Finally, the entropy method is used to dynamically determine the combined weights of each forecasting model and improve the adaptability of the model. Research and analysis using measured data from two wind farms located in northeast China have verified the effectiveness of the method.

Probabilistic prediction of short term wind power considering temporal and spatial dependence of prediction error

The short-term probabilistic prediction of wind power has the characteristics of spatial dependence and time-series dependence. Considering the two characteristics at the same time can improve the prediction level. In this paper, a probabilistic short-term wind power prediction model considering the temporal and spatial dependence of prediction error is proposed. Considering the coupling relationship between the two properties, the NWP(Numerical Weather Prediction) wind speed point prediction error in the historical period is hierarchical clustering, and the empirical distribution model is used to fit the probability distribution of the error under different wind conditions; the cumulative empirical distribution probability value corresponding to the NWP wind speed at the time to be predicted is bootstrap sampling; under the given confidence level, the possible wind speed at each time point to be predicted in the short term is calculated The power fluctuation range of the generator. The test results show that this method can ensure the statistical significance and fitting stability of the sub sample set at the same time, and improve the classification accuracy of the days to be predicted. Compared with considering a single property, the result of probability prediction performs better on multiple evaluation indexes.

A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting

With the growing penetration of wind power, the wind power forecasting is fundamental in aiding the grid scheduling and electricity trading. In this paper, a numerical weather prediction wind speed error correction model based on gated recurrent unit neural networks is proposed for short-term wind power forecasting. Firstly, the standard deviation of numerical weather prediction wind speed error is extracted as weights, and these weights are rearranged according to the numerical weather prediction wind speed time series to get the weight time series. Then, the bidirectional gated recurrent unit neural networks based error correction model is proposed to correct error of numerical weather prediction wind speed with the inputs as numerical weather prediction wind speed, trend and detail terms of the weight time series. The wind power curve model is applied to forecast short-term wind power by using corrected numerical weather prediction wind speed. Finally, the effectiveness of the proposed method is compared with benchmark models by using actual data of wind farm, and the results show that the proposed model outperforms these benchmark models.

NWP Combination Correction Model Based on Variable-weight Stacking Algorithm

Wind power forecasting (WPF) is of great significance for the balance of power system integrated the large-scale wind power and the economic benefit of wind farm. The error of WPF comes mainly from the NWP (Numerical Weather Prediction), which is the main input of WPF. In this paper, a combination correction model based on variable-weight stacking integration algorithm is proposed. First, the correlation coefficients between wind turbines (WTs) and their corresponding NWPs are calculated, and the NWP wind speed with the highest correlation is obtained as the model input. Second, the variable-weight stacking correction model is constructed, the sub-model includes multiple linear regression model (MLR), random forest regression model (RFR), BP neural network model (NN), and support vector machine model (SVM). To prevent overfitting, the cross-validation method is used in the sub-model training. The combination of each individual correction model is achieved using a nonlinear and variable-weight strategy. To validate the proposed model, data from a wind farm in North China are used. The results show that the proposed model outperforms the other benchmarks containing four traditional single models using single point data.

Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system

Little Numerical Weather Prediction (NWP) error may incur huge error in wind power forecasting due to the cubic relationship between wind speed and wind power. The current correction algorithms for NWP wind speed are all based on the priori statistics laws of NWP error at the same time, i.e., the mapping relationship between NWP error and NWP wind speed at time t + 1. However, this mapping relationship has strong random uncertainty, which limits the correction accuracy of existing algorithms. To address this problem, a sequence transfer correction algorithm (STCA) for NWP wind speed is proposed in this paper. In addition to the NWP wind speed at time t + 1, the measured wind speed at time t is also introduced as an input variable in the wind speed correction model, thus the sequence transfer relationship is incorporated, and the certainty of the mapping relationship between inputs and outputs of the correction model is improved. By applying STCA, 5 frequently-used models are established for correcting the NWP wind speed error. The actual operation data of two wind farms in northern and southern China are taken as examples for this study. The correction error for NWP wind speed is reduced by 0.2–1.5 m/s in wind farm 1 and 0–0.7 m/s in wind farm 2, when compared with the second-ranking algorithm model. It can be seen that the proposed correction algorithm for NWP wind speed has higher accuracy in both ultra-short-term and short-term time scales, and has strong generalization ability to different correction models. To validate that the proposed correction algorithm can be used for real applications, STCA is also applied to the wind power forecasting system for these 2 wind farms. Results show that the wind power forecasting accuracy is improved by 3.2–16.1% in wind farm 1 and 1.7–7.5% in wind farm 2.

Parameterizing surface wind speed over complex topography

AbstractSubgrid parameterizations are used in coarse‐scale meteorological and land surface models to account for the impact of unresolved topography on wind speed. While various parameterizations have been suggested, these were generally validated on a limited number of measurements in specific geographical areas. We used high‐resolution wind fields to investigate which terrain parameters most affect near‐surface wind speed over complex topography under neutral conditions. Wind fields were simulated using the Advanced Regional Prediction System (ARPS) on Gaussian random fields as model topographies to cover a wide range of terrain characteristics. We computed coarse‐scale wind speed, i.e., a spatial average over the large grid cell accounting for influence of unresolved topography, using a previously suggested subgrid parameterization for the sky view factor. We only require correlation length of subgrid topographic features and mean square slope in the coarse grid cell. Computed coarse‐scale wind speed compared well with domain‐averaged ARPS wind speed. To further statistically downscale coarse‐scale wind speed, we use local, fine‐scale topographic parameters, namely, the Laplacian of terrain elevations and mean square slope. Both parameters showed large correlations with fine‐scale ARPS wind speed. Comparing downscaled numerical weather prediction wind speed with measurements from a large number of stations throughout Switzerland resulted in overall improved correlations and distribution statistics. Since we used a large number of model topographies to derive the subgrid parameterization and the downscaling framework, both are not scale dependent nor bound to a specific geographic region. Both can readily be implemented since they are based on easy to derive terrain parameters.

Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting

Numerical weather prediction (NWP) of wind speed (WS) is an important input to wind power forecasting (WPF), which its accuracy will limit the WPF performance. This paper proposes three NWP correcting methods based on the multiple linear regression, a radial basis function neural network, and an Elman neural network. The proposed correction methods exhibit small sample learning and efficient computational ability. So, they are in favour of forecasting the performance of planned large-scale wind farms. To this end, a physical WPF model based on computational fluid dynamics is used to demonstrate the impact of improving the NWP WS data based forecasting. A certain wind farm located in China is selected as the case study, and the measured and NWP WS forecasts before and after correction are taken as inputs to the WPF model. Results show that all three correction methods improve the precision of the NWP WS forecasts, with the nonlinear correction models performing a little better than the linear one. Compared with the original NWP, the three corrected NWP WS have higher annual, single point, and short-term prediction accuracy. As expected, the accuracy of wind power forecasting will increase with the accuracy of the input NWP WS forecast. Moreover, the WS correction enhances the consistency of error variation trends between input WS and output wind power. The proposed WS correction methods greatly improve the accuracy of both original NWP WS and the WPF derived from them.