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To leverage the abundant wind energy resources available in mountainous regions, an increasing number of wind power facilities and associated transportation infrastructure are being constructed in these areas. There are different types of intense winds in mountainous areas and the non-linear characteristics of the different intense winds vary significantly. The periodic thermally-developed winds are the commonly occurring type of intense wind. Based on the comparison of wind characteristics, this research proposes a prediction model for the periodic thermally-developed winds. The research utilizes the Bi-directional Long Short-Term Memory model(Bi-LSTM), which has been optimized by the Bayesian optimization(BO), to predict wind speed. Based on the relationship between wind field characteristics in periodic thermally-developed winds, a regression correction module is proposed using a joint probability model. Additionally, to address the volatility and uncertainty of wind speed, a Gaussian mixture model is employed for probability prediction. The experimental results demonstrate several findings: 1. The BO-Bi-LSTM model accurately and reliably predicts multi-step results for periodic thermally-developed winds in mountainous areas. 2. The proposed correction module, when combined with other models, yields improved wind speed accuracy compared to the uncorrected values. 3. The Gaussian mixture model produces probabilistic prediction results that consider both interval coverage and interval bandwidth simultaneously. 4. The proposed model can successfully predict periodic thermally-developed winds in mountainous areas for practical engineering purposes, resulting in reliable probabilistic prediction outcomes. It is of great significance for the utilization of wind energy resources and the driving safety of vehicles on ancillary structures. [ABSTRACT FROM AUTHOR]

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. • TPSVR-HGN model proposed for heteroscedastic Gaussian noise. • Adding bias term broadens kernel function selection range in TPSVR-HGN. • TPSVR-HGN outperforms existing methods across diverse experimental datasets. [ABSTRACT FROM AUTHOR]

Reliable short-term wind speed prediction is critical for ensuring the rational exploitation and utilization of wind energy. However, due to complex characteristics (e.g., nonstationarity, nonlinearity, uncertainty, etc.) of natural winds, the realization of this task usually confronts a great challenge. For this purpose, an innovative method for forecasting short-term wind speeds is developed based on the principle of "decomposition-prediction-ensemble". Concretely, a new pretreatment technique, including boxplot figure based abnormal data diagnosis and correction, multivariate fast iterative filtering based time-frequency decomposition, and improved amplitude and frequency modulation based subseries reconstruction, is first developed to perform the high-quality data preprocessing. Then, three different algorithms in conjunction with the correntropy loss and consideration of model diversity are designed as high-performance predictors to capture more data characteristics. Further, a hybrid ensemble strategy combining stacking ensemble and hierarchical ensemble is developed to learn the potential interaction or nonlinear correlation among decomposed subseries as well as some uncertain information for high-reliability prediction. The eventual predictions are given in the form of deterministic point-value, interval, and real-time probability density function. Numerical examples based on four sets of multi-height wind speed data prove the effectiveness and superiority of the proposed method. For example, the average promotion obtained by this method compared with univariate conditional kernel density estimation in terms of mean absolute error is 33.87%, while the improvement in terms of coverage width criterion is 29.64%. • A new pretreatment technique is designed for high-quality data processing. • Three predictors with high robustness and strong generalization are developed. • Hybrid ensemble strategy can yield deterministic and probabilistic predictions. [ABSTRACT FROM AUTHOR]

Environmental pollution and energy depletion have spurred the exploration of renewable energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a competitive option. However, its effectiveness relies on accurately predicting the fluctuating wind speeds, necessitating ongoing research in this area. In order to improve the accuracy of wind speed prediction, this article presents the first Complex-valued version of the Artificial Hummingbird Algorithm (CAHA), which utilizes the idea of doubles to increase the initial population diversity and enhance the algorithm's performance. Additionally, the article employs the CAHA to optimize the parameters of Artificial Neural Networks (ANNs). This marks the first application of the hybrid model to address the short-term wind speed prediction problem. The experiments begin with a performance evaluation of CAHA using the CEC2022 test set. Then, the CAHA is employed to optimize the parameters of six distinct ANNs to select the ANN model that can produce the best prediction result when synergized with CAHA. The results determine that the hybrid model based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) exhibits the best prediction performance. Furthermore, the performance of CAHA is compared with other classical optimization algorithms in short-term wind speed prediction problems based on ANFIS. Statistical results show that the ANFIS-CAHA model significantly improves the accuracy of short-term wind speed prediction, making it a potent tool for the integration of wind energy into smart grid engineering. [ABSTRACT FROM AUTHOR]

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear characteristics of wind speed series. • A novel hybrid short-term wind speed prediction model is proposed. •The model of FEEMD-RT-PSR is used for the data preprocessing. •Whale optimization algorithm is optimized. •The performance of extreme learning machine is improved. [ABSTRACT FROM AUTHOR]

• A new improved RBF neural network model is proposed. • VMD-WT is used to extract features and remove noise of wind speed data. • The PCA-BP method is proposed to screen out the model input vectors. • The validity and applicability of the model are tested by data from two wind farms. Accurate short-term wind power forecasting is significant for rational dispatching of the power grid and ensuring the power supply quality. In order to enhance the accuracy of short-term wind speed prediction, a hybrid model based on VMD-WT and PCA-BP-RBF neural network is proposed. In data pre-processing period, the non-stationary wind speed sequence is decomposed into a number of relatively stationary intrinsic mode functions (IMF) by variational mode decomposition (VMD); then WT algorithm is used to perform secondary denoising on each IMF. At the same time, several factors affecting wind speed are introduced, from which the input features that participated in the prediction are selected by PCA-BP method. Next, the RBF neural network is utilized to predict each IMF. Finally, all IMF prediction results are aggregated to obtain the final wind speed value. Combining the data of Spanish and Chinese wind farms, the experiment results show that: (1) compared with EMD, VMD-WT can better solve the problems of modal aliasing and endpoint effect, which can make the periodic characteristics of each IMF more obvious, then promote the forecasting performance; (2) using PCA-BP method to filter the model input data, the redundant and irrelevant information is eliminated, the complexity of the model is reduced, and the predictive performance of RBF model is improved; (3) compared with other traditional models, the hybrid model proposed in this paper has greatly improved the accuracy in short-term wind speed forecasting. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Refined variational mode decomposition with adaptive level number is developed. • Conditional kernel density estimation with optimized bandwidth is proposed. • Based on above techniques, real time decomposition-based hybrid method is offered. • Deterministic and probabilistic predictions are provided by the hybrid method. • Case studies show hybrid method performs well in reliability and stability. Abstract Short-term wind speed prediction is an important task for the wind energy development. However, due to intermittency and uncertainty of wind resources, it is difficult to be achieved only by the deterministic prediction. Hence, there is a motivation to develop a novel method to further consider the uncertainty. The originality is to develop an innovative hybrid wind speed forecasting model based on empirical mode decomposition, variational mode decomposition, sample entropy and conditional kernel density estimation. More specifically, the original data are decomposed in real time by the refined variational mode decomposition, where the number of decomposition levels is adaptively determined by empirical mode decomposition and sample entropy. Then, conditional kernel density estimation with the bandwidth optimized by normal reference criterion is used to obtain the predictive probability density function for each subseries, by which the expectations and variances are derived. Finally, the deterministic prediction is produced by summarizing these expectations and the probabilistic prediction interval is obtained by the covariance and the Gaussian distribution assumption on target wind speed. It should be emphasized that some hypotheses are adopted in the proposed method: the correlations among different subseries can be characterized by the covariance and the target wind speed follows Gaussian distribution. Two case studies based on the measured data are used to evaluate the performance of the proposed method. The results show the improvement by the proposed method is up to 20% compared with empirical mode decomposition-based method. The main conclusion is that the proposed method may provide the better prediction over other models for the wind speed data with nonstationarity. [ABSTRACT FROM AUTHOR]

Accurately predicting wind speed is crucial towards maximizing the use of wind energy. Unfortunately, due to the non-stationary and intermittent nature of wind speed, reliable prediction has proven to be challenging. Properly exploring the inherent sequence connection between wind speed and its underlying frequency characteristics would be advantageous in enhancing forecast accuracy. Expanding on this concept, innovative models have been developed. Initially, they utilize multiple convolutions to capture the internal features of the time series data. This is followed by the application of the state frequency memory recurrent block to extract the relevant frequency properties. To facilitate the identification of crucial hidden elements, different attention mechanisms are utilized between these two modules which enables multi-resolution feature fusion. Real-world data is used to conduct multi-step short-term wind speed prediction tests, and various classical models are evaluated. The results indicate that our proposed models, which possess time-frequency mining capability, outperform other models in terms of forecast accuracy across four commonly used error indices. Additionally, a parameter sensitivity analysis is conducted on the prediction models to demonstrate the high stability of the proposed model. Furthermore, the working mechanism of our proposed model is thoroughly explored in order to confirm its exceptional prediction accuracy. [ABSTRACT FROM AUTHOR]

The short-term wind speed prediction is important for the connection of wind power to the grid. However, it is challenging due to the stochastic and intermittent characteristics of wind speed. In this paper, a novel wind speed prediction method named as EWT-ARIMA-LSSVM-GPR-DE-GWO method is proposed, where the empirical wavelet transform (EWT) is applied to decompose the original wind speed signal into multiple intrinsic mode functions (IMF), and various prediction models including the statistical model of autoregressive integrated moving average (ARIMA), artificial intelligence models of least squares support vector machine (LSSVM) and Gaussian process regression (GPR) are used for predicting the IMF in sequence from low frequency to high frequency based on the superiority of each model. Differential evolution-grey wolf optimizer (DE-GWO) algorithm is used to optimize the hyperparameters of artificial intelligence prediction models of LSSVM and GPR. The proposed short-term wind speed prediction method shows superior performance in both accuracy and stability with validation by the measurement data. Both the EWT signal decomposition and DE-GWO hyperparameter optimization are effective to promote the prediction accuracy for wind speeds with large non-stationary. • A novel short-term wind speed prediction method is proposed. • The effect of signal decompositon on the wind speed prediction is analyzed. • The hyperparameter optimization is used to improve the prediction performance. [ABSTRACT FROM AUTHOR]

Over the past few years, significant progress has been made in wind power generation worldwide. Because of the turbulent nature of wind velocity, the management of wind intermittence is a substantial field of research in the wind energy sector. This paper presents an investigation of this problem in two parts, the prediction of wind speed in both temporal and spatial dimensions, using artificial neural networks (ANNs). ANNs are novel methods applicable in modeling of complicated systems such as wind speed which generally investigated by a large amount of registered data exemplifying the behavior of. We first predicted the temporal dimension of wind speed at one-hour time interval, as a short-term wind speed prediction, in three wind observation stations (WOSs) in Iran. In the next part, estimation of wind speed data in a WOS using data from some other nearby WOSs was carried out. Due to the limitation of data collection, two groups of WOSs were selected for this target. The average value of the wind speed histogram error obtained from the best model in both groups is about 2.6% which is certainly promising. In Iran, the scarcity of meteorological data has resulted in the limited study of wind energy resources. Therefore, this type of spatial prediction is very useful in wind resource assessment in the Iranian wind energy industry. This is a valuable tool that enables the decision maker to precisely detect the high wind speed areas over an entire region in the first step of investigation. [ABSTRACT FROM AUTHOR]

The classical ridge regression technique makes an assumption that the noise is Gaussian. However, it is reported that the noise models in some practical applications do not satisfy Gaussian distribution, such as wind speed prediction. In this case, the classical regression techniques are not optimal. So we derive an optimal loss function and construct a new framework of kernel ridge regression technique for general noise model ( N-KRR ). The Augmented Lagrangian Multiplier method is introduced to solve N-KRR . We test the proposed technique on artificial data and short-term wind speed prediction. Experimental results confirm the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The safe and stable operation of wind power systems requires the support of wind speed prediction. To ensure the controllability and stability of smart grid dispatching, a novel hybrid model consisting of data-adaptive decomposition, reinforcement learning ensemble, and improved error correction is established for short-term wind speed forecasting. In decomposition module, empirical wavelet transform algorithm is used to adaptively disassemble and reconstruct the wind speed series. In ensemble module, Q-learning is utilized to integrate gated recurrent unit, bidirectional long short-term memory, and deep belief network. In error correction module, wavelet packet decomposition and outlier-robust extreme learning machine are combined to developing predictable components. An appropriate correction shrinkage rate is used to obtain the best correction effect. Ljung-Box Q-Test is utilized to judge the termination of the error correction iteration. Four real data are utilized to validate model performance in the case study. Experimental results show that: (a) The proposed hybrid model can accurately capture the changes of wind data. Taking 1-step prediction results as an example, the mean absolute errors for site #1, #2, #3, and #4 are 0.0829 m/s, 0.0661 m/s, 0.0906 m/s, and 0.0803 m/s, respectively; (b) Compared with several state-of-the-art models, the proposed model has the best prediction performance. • A reinforcement learning ensemble strategy is developed to determine the best model. • Three deep learning benchmark predictors are combined to learn the law of wind speed. • An improved error correction technique is proposed. • A correction shrinkage rate is introduced to reduce the risk of overfitting. [ABSTRACT FROM AUTHOR]

• Time varying filter-based empirical mode decomposition is used to tackle the data; • Three different nonlinear models are developed to grasp the nonlinear information; • Group method of data handling model is used to perform the combination prediction; • Case studies based on different datasets prove the superiority of the hybrid method. The realization of precise and reliable short-term wind speed prediction is extremely essential to wind power development, especially for its integration into traditional grid system. For this purpose, this study develops a novel forecasting method based on time varying filter-based empirical mode decomposition, auto-regressive integrated moving average model and group method of data handling-based hybrid model. This method mainly contains four individual steps for grasping the major behavioral characteristics of wind speed data. The first step adopts time varying filter-based empirical mode decomposition to handle the nonlinearity and nonstationarity of the raw wind speed data by decomposing them into a number of subseries with more stability and regularity. Then, auto-regressive integrated moving average model is applied to depict the linear characteristic hidden in the data. For the above modeling errors (i.e., the nonlinear residuals), the third step employs three nonlinear models with different action mechanisms (i.e., least square support vector machine, genetic programming algorithm and spatio-temporal radial basis function neural network) to systematically capture their complex nonlinear features. Finally, group method of data handling neural network is utilized to combine these nonlinear models and perform the selective prediction, where the involved models and their weights could be determined automatically. Four groups of the measured wind speed datasets with two different time intervals are used to assess the performance of the proposed method. The experimental results indicate it outperforms the other compared models and may have great potential for the practical application in power system. [ABSTRACT FROM AUTHOR]

• Robust local mean decomposition is used in wind speed prediction. • Group method of data handling for grasping the nonlinearity is developed. • Unbiased conditional kernel density estimation is used to explain the uncertainty. • Three case studies validate the performance of the proposed method. Short-term wind speed prediction has a great practical significance in wind power exploitation and development. However, owing to strong nonstationarity and nonlinearity of natural wind, it is hard to obtain accurate and reliable prediction by traditional models. To this end, this paper develops an innovative hybrid model to reveal the different characteristics of wind speed data for obtaining the satisfactory forecasting capability. Besides the deterministic prediction, this method could also provide probabilistic prediction. Specifically, robust local mean decomposition is firstly implemented to address the nonstationarity and nonlinearity of wind speed time series by separating them into a series of different subseries with lower ones; Then, an unexplored group method of data handling neural network is developed to extract the nonlinear information in each subseries and subsequently perform the corresponding individual prediction; Furthermore, for the above modeling residuals, traditional conditional kernel density estimation with the embedded dimension determined by partial autocorrelation function is employed to describe the other potential linear and nonlinear characteristics in the form of conditional probability, where the bandwidth matrices are optimized by normal reference criterion. On this basis, the deterministic prediction with the single-value is generated through aggregate calculation. In order to further consider the uncertainty in the data, the probabilistic prediction using unbiased conditional kernel density estimation is performed and the corresponding result is exhibited by prediction intervals. Case studies based on three groups of the measured wind speed data are conducted to comprehensively illustrate the effectiveness of the proposed method. The final results indicate that the proposed method occupies greater accuracy and higher reliability in comparison with the other concerned models. [ABSTRACT FROM AUTHOR]