Short-term Wind Speed Research Articles

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.

A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm

Wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, based on nonlinear auto-regressive model with exogenous inputs (NARX) and the hybrid chaos-cloud salp swarm algorithm (CC-SSA), a short-term wind speed prediction method is proposed. Firstly, to reduce the complexity of the original wind speed data and generate subcomponents with different patterns and low complexity, a mixed modal decomposition method is carried out by combining variational modal decomposition (VMD) based on Pearson correlation coefficient and generalized S-transform (GST) based on adaptive sample entropy. Therefore, the complementary advantages of different mode decompositions are obtained by combining the two different subcomponents into a mixed component. Secondly, by using cloud model and chaotic map, the improved CC-SSA algorithm is proposed to improve the convergence performance of salp swarm algorithm (SSA). Finally, by using CC-SSA algorithm to optimize the weights of NARX, the CC-SSA-NARX forecasting model is established to predict wind speed. The experimental results show that for the 1-Step, 2-Step, and 3-Step datasets of the actual wind speed in the studied region, the MAE, MAPE, RMSE, and R2 of the CC-SSA-NARX model are 0.23, 6 %, 0.27, and 0.97 respectively. The proposed model present the highest prediction index among the other 7 comparative models, showing high accuracy and generalization ability in short-term wind speed prediction, it can provide a certain reference for enhancing the stability of wind power generation and promoting the sustainable development of the wind power industry to some extent.

The Short-Term Price Elasticity, Temperature Elasticity, and Wind Speed Elasticity of Electricity: A Case Study from Norway

Energy production using hydropower has a 150-year history in Norway. High mountains, lots of rain, and a well-developed technology laid the foundation for low and stable electricity prices. The Norwegian electricity market is unique and different from any other country. Nearly all electricity produced (98.3 percent) comes from renewable energy sources and 75 percent of the energy used for heating is electricity. From autumn 2020, major changes have been observed in the electricity market in Norway. In 2021, Norway opened two transmission cables, one to Germany and one to England. Both cables have a capacity of 1400 MW. The average price per MWh was NOK 263 in southern Norway in the period 2013–2020, which more than quadrupled to NOK 1192 per MWh in the period 2021–2023. We have investigated how the market reacted to the large price increase. We found that price elasticity is low even when the price is very high. It is the temperature that controls the consumption. When it is cold—below zero degrees Celcius—the temperature elasticity is close to zero; the temperature elasticity is not constant. When the temperature is above zero, the temperature elasticity is about −0.7. Price variations or changes in wind speed only lead to minor adjustments in electricity consumption. It is the variations in temperature that result in the observable fluctuations in electricity consumption. Since Norway exports electricity to Sweden, Denmark, Finland, Germany, the Netherlands, and England, knowledge of the Norwegian electricity market is relevant for many market participants. The Norwegian electricity market differs from those in other countries. Therefore, there is a risk that conclusions drawn about the Norwegian electricity market based on research conducted in other countries may be incorrect or inaccurate. Our contribution with this case study is to deepen the knowledge of how the electricity market in Norway operates.

Analysis of short-term wind speed variation, trends and prediction: A case study of Tamil Nadu, India

Abstract Purpose The purpose of this research article is to analyze the short-term wind speed and develop a framework model to overcome the challenges in the wind power industry. Design/Methodology/Approach Real data with a case study of wind speed is presented to illustrate the advantages of this new wind speed analytical framework. Hourly measurements of wind speed are observed, and the experiments are conducted using tools such as ANOVA, control charts, trend analysis, and predictive models. The August month data for over 13 years from modern era retrospective-analysis for research and applications (MERRA) National aeronautics and space administration (NASA) for Coimbatore and Erode locations in Tamil Nadu, India, have been used. The results were considered for the study to understand the wind speed data and the implementation of new wind power projects in India. Findings The essence of the proposed wind speed analytical framework is its flexible approach, which enables the effective integration of wind firms’ individual requirements by developing tailor-made analytical evaluations. Originality/Value This article derives the wind speed analytical framework with the application of statistical tools and machine learning algorithms.

A new two-stage decomposition and integrated hybrid model for short-term wind speed prediction

Accurate wind speed prediction is of essential importance for the stability and safe operation of power systems. Given the complexity of wind speed sequence, this paper proposed a new two-stage decomposition and integrated hybrid model to improve the accuracy of wind speed prediction. A two-stage decomposition method combining robust local mean decomposition (RLMD), sample entropy (SE) and variational modal decomposition (VMD) was used to decompose the wind speed signal in the data preprocessing stage. Firstly, the wind speed signal was decomposed into various components by RLMD, and the complexity of each component was calculated using the SE to classify them into random, detail component and trend component. Then, a secondary decomposition of the random component with the highest SE was performed using the VMD. In the prediction stage, two different prediction models were used for prediction depending on the smoothness of each component. Stochastic configuration networks (SCN) was used to predict the detail and trend components with relatively smoothness. Echo state network (ESN) was used to predict the components of the secondary decomposition. Finally, the actual wind speed data were compared by different prediction models, which illustrated that the prediction method proposed in this paper had good prediction accuracy and generalizability.

Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy.

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.

Twin proximal support vector regression with heteroscedastic Gaussian noise

Twin proximal support vector regression, a novel approach, merges twin support vector regression (TSVR) with proximal support vector regression (PSVR) for regression analysis. Classical PSVR and TSVR assume that the data noise follows a homoscedastic Gaussian distribution with zero mean. However, in the real world, such as short-term wind speed prediction and wind farm power generation prediction, noise tends to follow a heteroscedastic Gaussian distribution with zero mean. Therefore, based on the aforementioned model framework and added the heteroscedastic noise feature, we develop a novel regression model named the twin proximal support vector regression model with heteroscedastic Gaussian noise (TPSVR-HGN). The Augment Lagrange multiplier approach is used to solve the proposed TPSVR-HGN model. On the artificial dataset and the real world dataset, compared with recent advanced models, the prediction accuracy of the TPSVR-HGN model has been improved by about 7% and 11% respectively. As the results of this experiment indicate, our method is effective and feasible.

Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Wind speed forecasting plays a crucial role in enhancing the efficiency, reliability, and profitability of renewable energy systems. Accurate wind speed forecasting optimizes energy production and grid integration in renewable energy systems. It assists in maintenance scheduling, decision-making in energy markets, and supports risk management for financial planning. This paper investigates the difficult balance between model complexity and forecasting accuracy in wind speed forecasting using Artificial Neural Networks (ANNs) and Wavelet Neural Networks (WNNs). Employing a time series approach, the study develops models for 1-h-ahead wind speed forecasting, utilizing recent averaged data and exploiting correlations between consecutive wind speeds for improved short-term predictions. Models training involve the Backpropagation algorithm, with careful input variable selection to minimize errors. This study emphasizes that simpler ANN and WNN models can outperform complex ones and explores the effectiveness of wavelet filtering techniques and demonstrates the benefits of using wavelet filtering to enhance forecasting accuracy. The robustness of the developed models is validated through K-fold cross-validation, confirming the efficacy of the proposed models. Evaluation across two datasets demonstrates the superiority of the proposed models over the persistence model, with enhancements of 7–14% in RMSE. Additionally, the efficacy of wavelet transforms in enhancing forecasting accuracy compared to regular neural network predictions is emphasized, underscoring the effectiveness of wavelet filtering offering a strategic approach for optimizing wind power management in electrical grids.

Improving short-term offshore wind speed forecast accuracy using a VMD-PE-FCGRU hybrid model

The integration of large-scale offshore wind power into the power grid presents significant challenges for grid operation and dispatch due to the variability and intermittency of offshore wind energy. However, non-stationarities and noise in wind speed time series pose challenges. This study proposes a VMD-PE-FCGRU methodology to address this challenge of predicting offshore wind speeds. Firstly, the Variational Mode Decomposition (VMD) algorithm decomposes the original wind speed signal, considering the strong fluctuations and high noise inherent to offshore wind energy. A corresponding performance evaluation index is established to assess the effectiveness of this deconstruction process. To further enhance the model's (GRU) ability to capture time series information, a Position Encoding layer (PE) has been added, and the network is deepened through a Fully Connected Neural Network (FCNN). This allows multi-dimensional time series features to be input into the Gated Recurrent Unit (GRU) network for forecast. The accuracy of the proposed method is then verified using measured historical data from the wind tower of an offshore wind farm in Guangdong. Experimental results demonstrate that the proposed method can accurately forecast short-term wind speeds for offshore wind farms, with a Mean Absolute Error (MAE) of 0.199 and a Mean Absolute Percentage Error (MAPE) of only 2.45%. Moreover, the qualified rate (r) reaches 100%, providing an accurate reference for real-time power grid dispatching and can inform decision-making for the integration of offshore wind power into the grid.

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

Evaluation and correction of wind energy forecast in Yangjiang

Wind power generation is one of the most promising new energy sources. It is important to study effective short-term wind speed forecasting methods. In this paper, the CMA-GD model is applied to forecast the wind speed over South China in four typical months (January, April, August, and October) in 2022. Taking Longyue Wind Farm in Yangjiang as an example, the simulated wind speed and wind direction are evaluated based on the observation of this station at four heights (10 m, 30 m, 50 m, and 70 m). The results show that the CMA-GD model has a good effect on forecasting the next day’s 24 h wind speed of Longyue Wind Farm. Overall, the correlation coefficients (R) between forecast and observation are 0.77-0.81, and the root-mean-square errors (RMSE) are 1.80-2.08 m·s-1. With the increase in altitude, the simulation effect is a little better. For different months, the R is as follows: October>August>April>January, and the RMSE and mean absolute error (MAE) are as follows: October>January>August>April. For diurnal variation, the wind speed simulation effect in the night is better than that in the day. Due to the influence of the subtropical monsoon climate and local mountain microclimate in April and August, there are some deviations in the wind speed simulation during 4-8 p.m. The difference in wind direction between forecast and observation are as follows: August<January<October<April, the average wind direction differences are between 2-17°, with little difference in the vertical direction. After the forecast wind speed is corrected by the random forest (RF) algorithm, the R of wind speed at each height is 0.92, and RMSEs are 1.07-1.27 m·s-1. The revised wind speed forecast has the same diurnal variation characteristics as the observation, indicating that the random forest algorithm can effectively reduce the model forecast error.

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.

A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage

ABSTRACT The accuracy of wind speed prediction is crucial for the efficient operation and scheduling of power grids. In recent years, many wind speed prediction methods have been proposed, but the results have always been unsatisfactory, and the model accuracy in experimental testing has always been overestimated. This study focuses on the problem of information leakage caused by the decomposition of the test and general training sets in traditional wind speed prediction methods. Using the original model without decomposition as the standard and the mean average (P MAE ) and mean squared (P MSE ) errors as evaluation metrics, the overestimation degree of information leakage on the model accuracy was quantified. The results show that when the test set is decomposed together, the accuracy of the model is significantly overestimated. Specifically, the overestimation of P MAE ranges from 40% to 55%, and that of P MSE is from 65% to 85%. In addition, a singular spectrum analysis (SSA) – rolling decomposition (RD) – convolutional neural network (CNN) – bidirectional gated recurrent unit (BiGRU) – attention mechanism (AM) model based on the RD method was proposed. First, SSA was used to denoise the wind speed sequence, and then RD was performed on the original sequence to provide input vectors for the neural network model. Then, the CNN – BiGRU – AM hybrid neural network module predicted the wind speed sequence. Finally, to suppress the impact of boundary effects on the model accuracy, a time-series extension strategy based on neural networks was incorporated into the model. An example analysis indicates that the SSA – RD – CNN – BiGRU – AM model can avoid information leakage compared with other traditional models.

Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition

Wind speed plays a pivotal role in ensuring the stability of power grid operations. However, the inherent high volatility and non-stationarity of wind patterns pose significant challenges to achieving accurate predictions. To tackle this issue and enhance the interpretability of existing wind speed prediction models, this study proposes an innovative short-term wind speed prediction model that combines two-stage decomposition, meteorological data feature engineering, adaptive differential evolution with optional external archive (JADE) algorithm, and temporal fusion transformers (TFT). To begin, the wind speed data is subjected to decomposition using improved complete ensemble empirical mode decomposition with adaptive noise (IEEMD), yielding multiple eigenmode functions. Subsequently, the nonlinear decomposition subsequence undergoes further decomposition into multiple submodes via empirical wavelet transform (EWT), followed by the careful screening of nonlinear quadratic decomposition submodes. Additionally, meteorological features are ingeniously reconstructed into combined and statistical meteorological features, enhancing the effectiveness of input features. Next, the hyperparameters of the TFT are meticulously optimized using the powerful JADE algorithm. Empirical results unequivocally demonstrate that the IEEMD-EWT-JADE-TFT model achieves remarkably high prediction accuracy. Meanwhile, this interpretative experimental process and its results chart a novel path for decision-makers seeking reliable wind speed forecasting processes and outcomes.

A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM

Due to the nonlinearity, fluctuation, and intermittency of wind speed, its accurate prediction is essential for improving efficiency in wind power operation systems. In this regard, a hybrid model that combines the rime optimization algorithm (RIME), variational mode decomposition (VMD), multi-headed self-attention (MSA) mechanism and long short-term memory (LSTM) is proposed for wind speed prediction. First, the number of modes and VMD penalty parameter are optimized with RIME, the optimized parameters are brought into the VMD to decompose the raw wind speeds, and a Lagrange multiplier and quadratic penalty function are introduced to obtain the input series. Then, a LSTM short-term wind speed prediction model is constructed based on the MSA mechanism and solved for the hidden states and weights of each layer of attention in the model. Finally, a ReLU activation function is used to activate the hidden states of the LSTM model, and a weighted sum vector is used as the final sequence representation, which is inputted to the output layer for specific prediction to obtain the short-term wind speed prediction results. To verify the effectiveness of the proposed model, wind speed data from four wind farms in Ningxia, China, and two sets of wind speed data from an M2 tower in the USA are selected, and 19 models are built to compare the performance of the proposed model. The results show that the proposed model outperforms other models on all datasets in terms of all five performance metrics, with smaller errors and higher prediction accuracy.

Wavelet decomposition and neural networks: a potent combination for short term wind speed and power forecasting

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.

Short-term wind speed forecasting based on recurrent neural networks and Levy crystal structure algorithm

Short-term wind speed forecasting accuracy is essential for power generating operations planning, energy market scheduling, wind power equipment maintenance and safety management. Nevertheless, predicting wind speed is highly challenging since wind speed is inherently non-stationary and nonlinear. This research addresses this issue through the development of an innovative paradigm for wind speed forecasting. Initially, the benchmark wind speed forecasting model is constructed by integrating the long short-term memory network, gate recurrent unit network, bi-directional long short-term memory network, and auto regressive integrated moving average model to capture sequential dependencies, seasonality and trends. Subsequently, for identifying the ideal parameters for both the neural network and statistical models, a grid search technique is applied. To further improve the accuracy of wind speed forecasting, Levy CryStal structure algorithm is employed to optimize the weights of the four models to ensure faster and more effectively reach optimal configuration. The optimization process starts with the GoodPoint set method for initial population optimization, adds the levy operator to avoid local optima, and the models expounded within this study exhibit a high degree of statistical confidence, registering at a 99% level of certainty when compared to established benchmark models by Diebold-Mariano test.

An Ensemble Approach to Short-Term Wind Speed Predictions Using Stochastic Methods, Wavelets and Gradient Boosting Decision Trees

Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR’s efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies.

Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.