Wind Speed Series Research Articles

Probabilistic Tide Calculation for Multi-Wind Farm Distribution Network Based on CATTSTS Model

Traditional probabilistic tide calculation methods often fall short in capturing the dynamic nature of these systems, leading to discrepancies between calculated and actual data. To address this, a dynamic probabilistic tide calculation model is proposed in this study, incorporating a wind farm probabilistic model enhanced by the CATTSTS model. This integration aims to optimize prediction accuracy by improving feature focusing and data fitting, thereby reducing prediction errors and providing more reliable data for power system adjustments and optimizations. Experimental results demonstrate that the model's predictions for wind speed series in multi-wind farm distribution networks closely match actual conditions, underscoring its practical applicability and reliability in real-world settings. Furthermore, the study highlights the impact of spatial and temporal correlations on model performance. This research contributes to advancing the management of renewable energy integration in power systems, offering insights for more informed decision-making and operational efficiency.

A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction

When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy.

Practical method for evaluating wind influence on autonomous ship operations (2nd report)

AbstractRecently, a considerable number of research and development projects have focused on automatic vessels. A highly realistic simulator is needed to validate control algorithms for autonomous vessels. For instance, when considering the automatic berthing/unberthing of a vessel, the effect of wind in such low-speed operations cannot be ignored because of the low rudder performance during slow harbor maneuvers. Therefore, a simulator used to validate an automatic berthing/unberthing control algorithm should be able to reproduce the time histories of wind speed and wind direction realistically. Therefore, in our first report on this topic, to obtain the wind speed distribution, we proposed a simple algorithm to generate the time series and distribution of wind speed only from the mean wind speed. However, for wind direction, the spectral distribution could not be determined based on our literature surveys, and hence, a simple method for estimating the coefficients of the stochastic differential equation (SDE) could not be proposed. In this study, we propose a new methodology for generating the time history of wind direction based on the results of Kuwajima et al.’s work. They proposed a regression equation of the standard deviation of wind direction variation for the mean wind speed. In this study, we assumed that the wind direction distribution can be represented by a linear filter as in our previous paper, and its coefficients are derived from Kuwajima’s proposed equation. Then, as in the previous report, the time series of wind speed and wind direction can be calculated easily by analytically solving the one-dimensional SDE. The joint probability density functions of wind speed and wind direction obtained by computing them independently agree well with the measurement results.

Handling Volatility and Nonlinearity in Wind Speed Data: A Comparative Analysis between ARIMA-GARCH and ARIMA-MLP

One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess the internal structure of the wind speed daily data in two stations in Johor; Senai and Mersing. Due to the existence of some nonlinearity features in the residuals part of ARIMA modelling, two nonlinear models were introduced to capture the internal structure of the residual data. Both conventional time series models; GARCH, and machine learning model; MLP was applied to model the residuals of ARIMA model. The out-sample performance in forecasting accuracy was compared between the ARIMA-GARCH model and the ARIMA-MLP model. The findings proves that MLP model has outperformed GARCH model in capturing the dynamics in the residual data by providing the lowest error measurements. Thus, the machine learning approaches has proven its superiority against the conventional time series nonlinear model in handling the nonlinearity in the daily wind speed series for wind speed forecasting.

Synthetic wind speed time series generation by dynamic factor model

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN

Accurate wind speed forecasting is of great significance for the utilization of Arctic wind energy resources. The traditional single model is difficult to fully depict the nonlinearity of wind speed and its wide range of variations. In this paper, a hybrid model is proposed for multi-step wind speed forecasting, which combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), convolutional neural network (CNN) and long-short term memory neural network (LSTM). The wind speed series is firstly decomposed into several intrinsic mode functions (IMF) by CEEMDAN to provide more stable data. Secondly, four-step-ahead forecasts are realized using well-tuned CNN-LSTM model for each IMF. Finally, the forecasted wind speed is obtained by reconstruction. The effectiveness and feasibility of the proposed method is validated based on thorough evaluation and step-by-step analysis. The RMSE of the proposed model is 0.4046 m/s, which are reduced by 58 % compared with 7 benchmark models. Furthermore, the average prediction interval of the proposed model is also reduced by 20 %, 16 % and 7 % compared to CEEMDAN-FCNN, CEEMDAN-CNN and CEEMDAN-LSTM respectively. The results prove that all three parts of the proposed model contribute to a better performance in wind speed forecasting.

Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model

Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed.

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism

Accurate wind speed forecasting is crucial for wind energy development and utilization. The non-linearity and non-stationarity of the wind speed leads to difficulties in its prediction. Recent years, plentiful forecasting approaches have been developed to improve the short-term wind speed forecasting. Among them, the ensemble system that consists of several sub-models is highly competitive. However, most of ensemble systems employ traditional data pre-processing method and integrate the outputs of sub-models with fixed weight assignment, which severely constrains the forecasting performance. To address these prevailing problems and further enhance the forecasting accuracy and stability, a novel ensemble system is proposed in this study, which comprising four modules: a data pre-processing module, an optimization module, a multiscale prediction module and a combination module. A hybrid decomposition approach is developed to fully exploit the changing modes of wind speed series in data pre-processing module. The multiscale prediction module employs advanced artificial intelligence models to conduct forecasting with the assistance of the optimization module. Finally, the self-attention mechanism is used to combine the outputs of sub-models. Measured hourly wind speed from four stations are adopted as datasets, and four metrics are used to evaluate the forecasting performance. Three comprehensive experiments are designed to explore the functionality and validate the predictive capability of the proposed ensemble system. The experimental results indicate that the proposed ensemble system significantly outperformed single models. The superiority of the proposed ensemble system is also demonstrated in comparisons with hybrid models and other ensemble systems. Overall, the proposed ensemble system can provide more accurate and reliable forecasting for the scheduling and management of wind power systems than other benchmark models.

Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming.

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

AbstractAccurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

Short-Term Probabilistic Wind Speed Predictions Integrating Multivariate Linear Regression and Generative Adversarial Network Methods

The precise forecasting of wind speeds is critical to lessen the harmful impacts of wind fluctuations on power networks and aid in merging wind energy into the grid system. However, prior research has predominantly focused on point forecasts, often overlooking the uncertainties inherent in the prediction accuracy. For this research, we suggest a new approach for forecasting wind speed intervals (PI). Specifically, the actual wind speed series are initially procured, and the complete ensemble empirical mode decomposition coupled with adaptive noise (CEEMDAN) method decomposes the actual wind speed series into constituent numerous mode functions. Furthermore, a generative adversarial network (GAN) is utilized to achieve the wind speed PI in conjunction with the multivariate linear regression method. To confirm the effectiveness of the suggested model, four datasets are selected. The validation results suggest that this suggested model attains a superior PI accuracy compared with those of numerous benchmark techniques. In the context of PI of dataset 4, the PINAW values show improvements of 68.06% and 32.35% over the CEEMDAN-CNN and VMD-GRU values in single-step forecasting, respectively. In conclusion, the proposed model excels over the counterpart models by exhibiting diminished a PINAW and CWC, while maintaining a similar PICP.

GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction

Wind speed prediction methods used to schedule wind power generation in advance is of great significance for ensuring grid safety and improving wind energy availability. However, most existing wind speed prediction models insufficiently extract spatial features for predicting wind speed at wind turbines stations, leading to less satisfying prediction results. To solve this issue, an adaptive spatiotemporal features fusion Transformer is proposed based on graph attention network (GAT) and optimizable graph matrix. First, a novel parameter optimization matrix is constructed using geographic information of cluster wind turbines, dynamic time warping (DTW), and maximum information coefficient (MIC) information to express the wind speed spatial correlation among these turbines. Second, graph attention network is used to extract spatial features from this matrix, sufficiently evaluating spatial similarity of wind speed series at different stations. Third, an embedding block is performed to characterize temporal features of cluster wind speed. Fourth, to effectively integrate these spatial and temporal features into spatial-temporal features, a new type of entangle block based on parameter optimization is proposed. Fifth, predicting values are obtained based on an improved Transformer by extracting effective features from spatiotemporal features with multi-head attention mechanism. Finally, Huber loss function is used to iteratively optimize the network parameters of the proposed model. To verify the effectiveness of the proposed model, five performance indexes, including MAE, MSE, MAPE, TIC and IA are employed. The results show that the proposed model outperforms other models including GAT-GRU, GAT-LSTM, GAT-Transformer, GCN-GRU, GCN-LSTM, GCN-Transformer, and Autoformer.

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

ABSTRACT Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

Stochastic Modeling of Wind Derivatives with Application to the Alberta Energy Market

Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of European put style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG.

A Hybrid Approach Based on Unequal Span Segmentation-Clustering for Short-Term Wind Power Forecasting

Because the fluctuation and uncertainty of wind power generation bring severe secure and economic challenges to power systems, wind power forecasting becomes the critical part of the management in power systems. In this paper, a hybrid approach based on unequal span segmentation-clustering is proposed to mine the variation trend information in the wind speed series for improving forecasting performance. Firstly, the wind speed series representation is proposed to accurately represent the major variation trend of the wind speed series. It reduces the influence of the non-stationary and irregular behaviors of the wind speed series on the unequal span segmentation-clustering. Secondly, the shape-position comprehensive evaluation is proposed to combine shape and position measures to evaluate the clustering of the trend segments with unequal length. It identifies the trend segments which have comprehensive similarity on both shape and position to better construct forecasting models. Thirdly, novel bayesian optimization mutation operators are proposed to optimally move or add cut points in the unequal span segmentation. It enhances the local search capability of the unequal span segmentation. By comparing different approaches using wind datasets from two wind farms, the effectiveness and advantages of the proposed approach are demonstrated.

The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer

Accurate wind speed prediction is crucial for effective wind power grid integration and energy dispatching. Recent research has explored the combination of decomposition algorithms with forecast models to form hybrid models, aiming to enhance wind speed prediction accuracy. However, these traditional decomposition techniques often lead to high time costs in practical applications, as they require new wind speed sequences to be appended to historical long sequences for decomposition before entering the forecast model. To overcome this challenge, this study introduces and improves the Autoformer model, applying it for the first time to long-term univariate wind speed forecasting. By incorporating decomposition technology as a sub-module of the forecast model, Autoformer not only solves the high time cost issue found in conventional hybrid models but also retains the benefits of decomposition technology in time series processing.Furthermore, in this paper, the decomposition module of Autoformer is replaced with the Mixture of Expert Decomposition Module (MOEDecomp) to better extract complex trend elements of wind speed series. Combined with the auto-correlation mechanism, sequential attention is paid to wind speed for extracting time dependencies in long series. Additionally, the Wavelet Soft Threshold Denoising (WSTD) algorithm is utilised for noise reduction in wind speed sequences. To evaluate the model's performance, two multi-step forecasting strategies were employed to predict wind speeds for the forthcoming 24, 48, 72, 96, and 120 h using four datasets. Experimental results demonstrate that the proposed model surpasses 15 comparative models in terms of prediction accuracy, generalization ability, and handling uncertain data.

Study of the Characteristics of the Long-Term Persistence of Hourly Wind Speed in Xinjiang Based on Detrended Fluctuation Analysis

Profound research on the characteristics of the long-term persistence of wind is greatly significant for understanding the characteristics of wind speed mechanisms as well as for avoiding disasters caused by wind. In the current study, we selected the hourly 10 min wind speed series between 2017 and 2021 from 105 nation-level meteorological stations in Xinjiang and investigated the spatiotemporal variations in the long-term persistence of wind speed in different regions of Xinjiang and in different seasons using detrended fluctuation analysis. The main findings are as follows: (1) The wind speed in Xinjiang shows noticeable annual and seasonal variations, exhibiting satisfactory long-term sustainability. Winter has the best long-term sustainability, followed sequentially by spring, autumn, and summer because of wind speed stability. (2) The long-term persistence of hourly wind speed in Xinjiang exhibits remarkable regionality, with regions with strong wind superior to the remaining regions. (3) The long-term persistence of wind speed within the same season is primarily associated with wind speed magnitude and the dispersion degree between 90% and 100% of the wind speed numerical values. A higher wind speed indicates better long-term persistence. At the same speed, the more discrete the numerical values in the 90–100% distribution range, the better the persistence.

A model reference adaptive control framework for floating offshore wind turbines with collective and individual blade pitch strategy

The utilization of floating offshore wind turbines is regarded as a promising solution for offshore renewables, wherein blade pitch control plays a crucial role in both operation and maintenance. Among the various blade pitch control methods, strategies that involve more states pertaining to the control objectives demonstrate superior tracking performance. However, these approaches necessitate additional linearization and parameter tuning efforts, particularly when addressing varying operating conditions in real-world unsteady environments. In this study, a decoupled model reference adaptive control framework with inner control laws for ideal responses for both collective and individual blade pitch is employed, in the context of floating offshore wind turbine control problems during operation beyond rated conditions. According to the simulations conducted on a benchmark wind turbine simulator under a series of turbulent wind speeds, the proposed integrated controller achieved reductions in power fluctuation and damage equivalent load in blades compared to corresponding baseline controllers, at the expense of acceptable deterioration in tower base loads and blade actuator activities. The proposed scheme demonstrates its adaptability in serving the entire operating range, mitigating the need for parameter tuning due to variations in wind speed, and exhibits potential to involve more control purposes.