An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo i...An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function （PIRBF） and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network （MK-RPINN） for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.展开更多
Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power.Temporal-spatial wind speed features contain rich information;however,...Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power.Temporal-spatial wind speed features contain rich information;however,their use to predict wind speed remains one of the most challenging and less studied areas.This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attentionbased long short-term memory(LSTM),termed 2Attn-LSTM,a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data.To eliminate the unevenness of the original wind speed,we initially decompose the preprocessing data into IMF components by variational mode decomposition(VMD).Then,it encodes the spatial features of IMF components at the bottom of the model and decodes the temporal features to obtain each component's predicted value on the second layer.Finally,we obtain the ultimate prediction value after denormalization and superposition.We have performed extensive experiments for short-term predictions on real-world data,demonstrating that 2Attn-LSTM outperforms the four baseline methods.It is worth pointing out that the presented 2Atts-LSTM is a general model suitable for other spatial-temporal features.展开更多
As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.Howeve...As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.However,due to the stochastic and un-certain nature of wind energy,more accurate forecasting is necessary for its more stable and safer utilisation.This paper proposes a Legendre multiwavelet‐based neural network model for non‐linear wind speed prediction.It combines the excellent properties of Legendre multi‐wavelets with the self‐learning capability of neural networks,which has rigorous mathematical theory support.It learns input‐output data pairs and shares weights within divided subintervals,which can greatly reduce computing costs.We explore the effectiveness of Legendre multi‐wavelets as an activation function.Mean-while,it is successfully being applied to wind speed prediction.In addition,the appli-cation of Legendre multi‐wavelet neural networks in a hybrid model in decomposition‐reconstruction mode to wind speed prediction problems is also discussed.Numerical results on real data sets show that the proposed model is able to achieve optimal per-formance and high prediction accuracy.In particular,the model shows a more stable performance in multi‐step prediction,illustrating its superiority.展开更多
Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a...Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.展开更多
Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal netw...Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.展开更多
Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic...Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.展开更多
Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(...Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.展开更多
This paper presents and evaluates two novel ordinal classification methods for wind speed prediction,considering three prediction time-horizons:1h,4h,and 8h.To address the problem,wind speed values are discretised int...This paper presents and evaluates two novel ordinal classification methods for wind speed prediction,considering three prediction time-horizons:1h,4h,and 8h.To address the problem,wind speed values are discretised into four classes,critical for wind farm management.Each class represents essential information for wind farm production,ranging from very low wind speeds to extreme wind speed events and the corresponding production conditions,facilitating operational decisions for wind farm operators.Ordinal classifiers are more suitable than nominal methods to tackle this problem.The study’s primary objective is to compare recently proposed ordinal classifiers for addressing the challenges of wind speed prediction with a focus on extreme wind conditions,which are responsible for many turbine shutdowns.Hourly wind speed measurements from a Spanish wind farm and predictor variables from the European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5 Reanalysis)model are used.The proposed methods include an Artificial Neural Network(ANN)model implementing the Cumulative Link Model as an ordinal output function(MLP-CLM^(O)),which emphasises overall performance,and an ANN model optimised using a soft labelling technique based on triangular distributions(MLP-T^(O)),which excels at handling extreme class performance.The results demonstrate the superiority of both approaches over other nominal and ordinal methods across performance metrics that account for the unbalanced nature and ordinality of the data.MLP-CLMO excels in overall and ordinal performance,while MLP-TO demonstrates superior handling of the extreme class predictions.展开更多
Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying N...Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this pap...Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.展开更多
Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their dail...Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.展开更多
Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a...Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.展开更多
In this paper,a dual deep Q-network(DDQN)energy management model based on long-short memory neural network(LSTM)speed prediction is proposed under the model predictive control(MPC)framework.The initial learning rate a...In this paper,a dual deep Q-network(DDQN)energy management model based on long-short memory neural network(LSTM)speed prediction is proposed under the model predictive control(MPC)framework.The initial learning rate and neuron dropout probability of the LSTM speed prediction model are optimized by the genetic algorithm(GA).The prediction results show that the root-mean-square error of the GA-LSTM speed prediction method is smaller than the SVR method in different speed prediction horizons.The predicted demand power,the state of charge(SOC),and the demand power at the current moment are used as the state input of the agent,and the real-time control of the control strategy is realized by the MPC method.The simulation results show that the proposed control strategy reduces the equivalent fuel consumption by 0.0354 kg compared with DDQN,0.8439 kg compared with ECMS,and 0.742 kg compared with the power-following control strategy.The difference between the proposed control strategy and the dynamic planning control strategy is only 0.0048 kg,0.193%,while the SOC of the power battery remains stable.Finally,the hardware-in-the-loop simulation verifies that the proposed control strategy has good real-time performance.展开更多
Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting ...Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.展开更多
With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed...With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory(NSWLSTM) network and Gaussian process regression(GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory(LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR(NSWLSTM-GPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.展开更多
Purpose–The purpose of this paper is to improve the control precision of the station-keeping control for a stratosphere airship through the feedforward-feedback PID controller which is designed by the wind speed pred...Purpose–The purpose of this paper is to improve the control precision of the station-keeping control for a stratosphere airship through the feedforward-feedback PID controller which is designed by the wind speed prediction based on the incremental extreme learning machine(I-ELM).Design/methodology/approach–First of all,the online prediction of wind speed is implemented by the I-ELM with rolling time.Second,the feedforward-feedback PID controller is designed through the position information of the airship and the predicted wind speed.In the end,the one-dimensional dynamic model of the stratosphere airship is built,and the controller is applied in the numerical simulation.Findings–Based on the conducted numerical simulations,some valuable conclusions are obtained.First,through the comparison between the predicted value and true value of the wind speed,the wind speed prediction based on I-ELM is very accurate.Second,the feedforward-feedback PID controller designed in this paper is very effective.Originality/value–This paper is very valuable to the research of a high-accuracy station-keeping control of stratosphere airship.展开更多
Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the litera...Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very reliable.Therefore,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed prediction.The proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation learning.This reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution time.Furthermore,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy.The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique.The lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.展开更多
Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to pr...Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.展开更多
This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two win...This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).展开更多
文摘An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function （PIRBF） and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network （MK-RPINN） for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.
基金This work is supported in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions,Natural Science Foundation of China(No.61103141,No.61105007 and No.51405241)NARI Nanjing Control System Ltd.(No.524608190024).
文摘Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power.Temporal-spatial wind speed features contain rich information;however,their use to predict wind speed remains one of the most challenging and less studied areas.This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attentionbased long short-term memory(LSTM),termed 2Attn-LSTM,a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data.To eliminate the unevenness of the original wind speed,we initially decompose the preprocessing data into IMF components by variational mode decomposition(VMD).Then,it encodes the spatial features of IMF components at the bottom of the model and decodes the temporal features to obtain each component's predicted value on the second layer.Finally,we obtain the ultimate prediction value after denormalization and superposition.We have performed extensive experiments for short-term predictions on real-world data,demonstrating that 2Attn-LSTM outperforms the four baseline methods.It is worth pointing out that the presented 2Atts-LSTM is a general model suitable for other spatial-temporal features.
基金funded by Fundamental and Advanced Research Project of Chongqing CSTC of China(No.cstc2019jcyj‐msxmX0386 and No.cstc2020jcyj‐msxmX0232)National Statistical Science Research Project(No.2020LY100).
文摘As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.However,due to the stochastic and un-certain nature of wind energy,more accurate forecasting is necessary for its more stable and safer utilisation.This paper proposes a Legendre multiwavelet‐based neural network model for non‐linear wind speed prediction.It combines the excellent properties of Legendre multi‐wavelets with the self‐learning capability of neural networks,which has rigorous mathematical theory support.It learns input‐output data pairs and shares weights within divided subintervals,which can greatly reduce computing costs.We explore the effectiveness of Legendre multi‐wavelets as an activation function.Mean-while,it is successfully being applied to wind speed prediction.In addition,the appli-cation of Legendre multi‐wavelet neural networks in a hybrid model in decomposition‐reconstruction mode to wind speed prediction problems is also discussed.Numerical results on real data sets show that the proposed model is able to achieve optimal per-formance and high prediction accuracy.In particular,the model shows a more stable performance in multi‐step prediction,illustrating its superiority.
基金supported by the National Natural Science Foundation of China(61473176,61402260,61573225)the Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities(ZR2015JL021,ZR2015JL003)the Open Program from the State Key Laboratory of Management and Control for Complex Systems(20140102)
文摘Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.
基金supported by the undergraduate training program for innovation and entrepreneurship of NUIST(XJDC202110300239).
文摘Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.
基金the National Natural Science Foundation of China(No.61461027,61762059)the Provincial Science and Technology Program supported the Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA226)。
文摘Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.
基金Indian Institute of Technology Bombay for providing funding (Project code:13IRCCSG001)
文摘Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.
基金supported by the“Agencia Estatal de Investigación(España)”,Spanish Ministry of Science,Innovation and Universities(grant refs.:PID2023-150663NB-C21 and PID2023-150663NB-C22/AEI/10.13039/501100011033)by the European Com-mission,projects“CLImate INTelligence:Extreme events detection,attribution and adaptation design using machine learning,CLINT”(grant ref.:H2020-LC-CLA-2020-2,101003876)+6 种基金“Test and Exper-iment Facilities for the Agri-Food Domain,AgriFoodTEF”(grant ref.:DIGITAL-2022-CLOUD-AI-02,101100622)by the ENIA International Chair in Agriculture,University of Córdoba(grant ref.:TSI-100921-2023-3)by the European Union through the European Regional De-velopment Fund“project IA-CONV”funded by the Community of Madrid through the grant agreement for the promotion and advance-ment of research and technology transfer at the University of Alcalá(grant ref.:CM/DEMG/2024-039)by the University of Córdoba through competitive grants for Andalusian society challenges(grant ref.:PP2F_L1_15)Antonio Manuel Gómez-Orellana has been supported by“Consejería de Transformación Económica,Industria,Conocimiento y Universidades de la Junta de Andalucía”(grant ref.:PREDOC-00489)David Guijo-Rubio has been supported by the“Agencia Estatal de Inves-tigación(España)”MCIU/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR(grant ref.:JDC2022-048378-I)。
文摘This paper presents and evaluates two novel ordinal classification methods for wind speed prediction,considering three prediction time-horizons:1h,4h,and 8h.To address the problem,wind speed values are discretised into four classes,critical for wind farm management.Each class represents essential information for wind farm production,ranging from very low wind speeds to extreme wind speed events and the corresponding production conditions,facilitating operational decisions for wind farm operators.Ordinal classifiers are more suitable than nominal methods to tackle this problem.The study’s primary objective is to compare recently proposed ordinal classifiers for addressing the challenges of wind speed prediction with a focus on extreme wind conditions,which are responsible for many turbine shutdowns.Hourly wind speed measurements from a Spanish wind farm and predictor variables from the European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5 Reanalysis)model are used.The proposed methods include an Artificial Neural Network(ANN)model implementing the Cumulative Link Model as an ordinal output function(MLP-CLM^(O)),which emphasises overall performance,and an ANN model optimised using a soft labelling technique based on triangular distributions(MLP-T^(O)),which excels at handling extreme class performance.The results demonstrate the superiority of both approaches over other nominal and ordinal methods across performance metrics that account for the unbalanced nature and ordinality of the data.MLP-CLMO excels in overall and ordinal performance,while MLP-TO demonstrates superior handling of the extreme class predictions.
基金supported by Ningxia Natural Science Foundation Project(2023AAC02088)Liangshan Prefecture 2023 Annual Science and Technology Planning Project(23ZDYF0182).
文摘Under the context of global climate change,the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture.Using hourly meteorological data from the Sanying National Station and the Guyuan Greenhouse Station between April 2024 and April 2025,this study employed machine learning methods to develop wind speed prediction models based on BP neural network,support vector machine,and random forest(referred to as BP,SVM,and RF models),aiming to provide references for local disaster prevention and mitigation.The results indicate that:1)Wind speed at the Guyuan Greenhouse Station exhibits the strongest correlation with that at the National Station(0.489-0.595),followed by temperature and 24-hour precipitation(0.116-0.336).2)The mean absolute error(MAE)of the BP,RF,and SVM models at all heights is below 1.5 m/s,the root mean square error(RMSE)is under 2.1 m/s,and the forecast accuracy(FA)exceeds 75%,indicating satisfactory model performance.Compared to 3 m,the MAE and RMSE of 0.5 m are larger,while the FA is smaller.This indicates that the wind speed of 0.5 m is close to the ground,and is more affected by surface roughness and turbulence effects,resulting in greater randomness and making the model more difficult.3)Based on case analyses of May 10 and May 1,2024,the overall simulation performance ranks as“RF model>SVM model>BP model”;however,the SVM model demonstrates higher accuracy in simulating strong wind events.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
文摘Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.
基金National Natural Science Foundation of China(Grant No.51775478)Hebei Provincial Natural Science Foundation of China(Grant Nos.E2016203173,E2020203078).
文摘Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51905419 and 51175419)。
文摘Advanced vehicular control technologies rely on accurate speed prediction to make ecological and safe decisions. This paper proposes a novel stochastic speed prediction method for connected vehicles by incorporating a Bayesian network(BN) and a Back Propagation(BP) neural network. A BN model is first designed for predicting the stochastic vehicular speed in a priori. To improve the accuracy of the BN-based speed prediction, a BP-based predicted speed error compensation module is constructed by formulating a mapping between the predicted speed and its corresponding prediction error. In the end, a filtering algorithm is developed to smoothen the compensated stochastic vehicular speed. To validate the workings of the proposed approaches in experiments, two typical scenarios are considered: one predecessor vehicle in a double-vehicle scenario and two predecessor vehicles in a multi-vehicle scenario. Simulation results under the considered scenarios demonstrate that the proposed BN-BP fusion method outperforms the BN-based method with respect to the root mean square error, standardized residuals, and R-squared, and the online prediction time of proposed fusion prediction can satisfy a real-time application requirement. The main highlighted contributions of this article are threefold:(1) We put forward an improved BN method, which is combined with a BP neural network, to construct a stochastic vehicular speed prediction method under connected driving;(2) different from existing methods, a unique interconnected framework that consists of a stochastic vehicular speed prediction module, a compensation module, and a speed smoothing module is proposed;(3) extensive simulation studies based on a set of evaluation metrics are illustrated to reveal the advantages and merits of the proposed approaches.
基金supported by the National Natural Science Foundation of China(No.52175236)Research and development and demonstration application of heavy-duty diesel vehicle exhaust emission testing technology,China(No.24-8-cspz-18-nsh)Qingdao Civi Science and Technology Plan,China(No.19-6-1-88-nsh).
文摘In this paper,a dual deep Q-network(DDQN)energy management model based on long-short memory neural network(LSTM)speed prediction is proposed under the model predictive control(MPC)framework.The initial learning rate and neuron dropout probability of the LSTM speed prediction model are optimized by the genetic algorithm(GA).The prediction results show that the root-mean-square error of the GA-LSTM speed prediction method is smaller than the SVR method in different speed prediction horizons.The predicted demand power,the state of charge(SOC),and the demand power at the current moment are used as the state input of the agent,and the real-time control of the control strategy is realized by the MPC method.The simulation results show that the proposed control strategy reduces the equivalent fuel consumption by 0.0354 kg compared with DDQN,0.8439 kg compared with ECMS,and 0.742 kg compared with the power-following control strategy.The difference between the proposed control strategy and the dynamic planning control strategy is only 0.0048 kg,0.193%,while the SOC of the power battery remains stable.Finally,the hardware-in-the-loop simulation verifies that the proposed control strategy has good real-time performance.
基金the Gansu Province Soft Scientific Research Projects(No.2015GS06516)the Funds for Distinguished Young Scientists of Lanzhou University of Technology,China(No.J201304)。
文摘Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.
基金supported by the National Key Research and Development Programe of China (2016YFB0901900)the National Natural Science Foundation of China (U1908213)+2 种基金the Fundamental the Research Funds for the Central Universities(N182303037)Colleges and Universities in Hebei Province Science Research Program (QN2020504)the Foundation of Northeastern University at Qinhuangdao (XNB201803)。
文摘With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory(NSWLSTM) network and Gaussian process regression(GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory(LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR(NSWLSTM-GPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.
基金This work is supported by National Natural Science Foundation(NNSF)of China under Grant Nos 61603320,61273199 and 61673327.
文摘Purpose–The purpose of this paper is to improve the control precision of the station-keeping control for a stratosphere airship through the feedforward-feedback PID controller which is designed by the wind speed prediction based on the incremental extreme learning machine(I-ELM).Design/methodology/approach–First of all,the online prediction of wind speed is implemented by the I-ELM with rolling time.Second,the feedforward-feedback PID controller is designed through the position information of the airship and the predicted wind speed.In the end,the one-dimensional dynamic model of the stratosphere airship is built,and the controller is applied in the numerical simulation.Findings–Based on the conducted numerical simulations,some valuable conclusions are obtained.First,through the comparison between the predicted value and true value of the wind speed,the wind speed prediction based on I-ELM is very accurate.Second,the feedforward-feedback PID controller designed in this paper is very effective.Originality/value–This paper is very valuable to the research of a high-accuracy station-keeping control of stratosphere airship.
文摘Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions.Although there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very reliable.Therefore,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed prediction.The proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation learning.This reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution time.Furthermore,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy.The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique.The lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.
文摘Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.
基金National Key Research and Development Program of the Ministry of Science(2018YFB1502801)Hubei Provincial Natural Science Foundation(2022CFD017)Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)。
文摘This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).
