Accurate Wind Speed Forecasting Research Articles

STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting

As a renewable and clean energy source, wind energy has caught worldwide attention. To ensure the reliability and stability of wind energy production, wind speed forecasting is of great importance. Although existing forecasting models have shown promising results, they remain relatively small in scale and are incapable of learning comprehensive sequential representations from large-scale data to reason intricate spatio-temporal patterns obscured in entangled wind series. Recently, pre-trained large models have demonstrated impressive performance in natural language processing (NLP) and computer vision (CV). However, large models are rarely applied to wind speed forecasting due to the lack of large-scale wind data for training. To resolve this issue, we propose Spatio-Temporal Enhanced Pre-Trained Large Language Model, namely STELLM, innovatively leveraging the powerful reasoning ability of Large Language Models (LLMs) for accurate wind speed forecasting. Specifically, we first perform the series decomposition to separate wind series data into seasonal and trend components, capturing the distinct properties from disentangled patterns. Then, to empower LLMs with the spatio-temporal modeling capability, we introduce the spatial and temporal prompts, which fuse the geographic distribution information and short-term temporal patterns of wind turbines to enrich the input wind series. Moreover, we leverage an autoregressive fine-tuning strategy to align LLMs with wind series data and learn patch-level representations. Afterwards these representations are fed into a localized spatial module to capture the spatial dependencies between wind turbines. Extensive experiments on four public wind datasets demonstrate the performance superiority of STELLM.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of “classical” and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the “features” (intended as “time steps”), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Multistep short‐term wind speed prediction with rank pooling and fast Fourier transformation

AbstractShort‐term wind speed prediction is essential for economical wind power utilization. The real‐world wind speed data are typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multistep wind speed prediction, namely, LR‐FFT‐RP‐MLP/LSTM (linear fast Fourier transform rank pooling multiple‐layer perceptron/long short‐term memory). Our hybrid model processes the local and global input features simultaneously. We leverage RP for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit FFT to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are, respectively, integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared with state‐of‐the‐art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.

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.

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.

Evaluation of LSTM and Wavelet Methods for Wind Speed Forecasting in Bahia, Brazil

The pursuit of sustainable development is intricately linked to the effective management of renewable energy resources, with wind energy emerging as a key player on the global stage. However, the inherent volatility and intermittency of wind patterns pose significant challenges to accurate wind speed forecasting, which is crucial for the stable operation of wind turbines. Our study presents a novel forecasting model that integrates cuttingedge data decomposition techniques with Long Short-Term Memory (LSTM) networks in this context. Our approach leverages advanced methodologies such as Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and Enhanced Empirical Mode Decomposition (EEMD) to segment time series data into distinct high and low-frequency components. These segmented signals are then individually forecasted using Bidirectional LSTM (BiLSTM) networks, with the amalgamation of these predictions providing the final forecast output. Our empirical findings demonstrate that the hybrid model, particularly utilizing EMD and EEMD, exhibits superior performance compared to existing forecasting models in terms of both accuracy and stability. By effectively combining sophisticated data decomposition techniques with state-of-the-art deep learning algorithms, our proposed model offers a robust solution for wind speed forecasting. This facilitates the efficient management of renewable energy resources and advances the cause of sustainable development initiatives worldwide.

SVMD-TF-QS: An efficient and novel hybrid methodology for the wind speed prediction

Wind power is gaining significant attention as a renewable and environmentally friendly energy source. However, accurate forecasting of wind speed poses challenges due to its inherent variability and stochastic nature. To address this issue, a novel hybrid model (SVMD-TF-QS) for wind speed prediction (WSP) is proposed in this study. The model combines successive variational mode decomposition (SVMD) with a Transformer (TF) based model that incorporates a novel query selection (QS) mechanism. The SVMD component of the hybrid model offers several improvements, including enhanced mode extraction, adaptive mode determination, robustness against initial values of center frequencies, and improved computational efficiency. By decomposing the wind speed data using SVMD, the transformed data is then fed into the TF-QS model. The proposed approach effectively combines the benefits of the QS mechanism and the Transformer model to accurately predict wind speed while minimizing computational load. This is achieved by introducing a deterministic algorithm within the QS mechanism, which computes a sparse approximation of the attention matrix used in the Transformer model. This further enhances the predictive capabilities of the hybrid model. To evaluate its performance and generalization capability, extensive assessments are conducted using data from two wind farms located in Leicester and Portland. The assessments cover various time periods, including 5 min, 10 min, 15 min, 30 min, 1 h, and 2 h WSP intervals. The results of this study provide robust evidence supporting the effectiveness of the proposed hybrid model in WSP for the diverse wind farms and scenarios.

WGformer: A Weibull-Gaussian Informer based model for wind speed prediction

Accurate wind speed forecasting can improve energy management efficiency and promote the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind speed make prediction challenging. To address these issues, we design an efficient Informer-based model, with improved calculation speed, forecasting accuracy and generalization ability. The proposed model in this paper reasonably integrates the Weibull-Gaussian transform, Informer and kernel mean square error loss and addresses the combination of various components. The Weibull-Gaussian transform is used as the data preprocessing module, which can remove non-Gaussian characteristics from the original data, and thus achieve noise reduction. The Informer is used as the main predictor, which can efficiently output accurate forecasting results based on an encoder-decoder architecture and self-attention mechanism. The kernel mean square error loss function, which shows strong robustness to outliers, is used to evaluate the nonlinearity of errors in reproducing kernel Hilbert space. To evaluate the performance of the proposed model, it is compared with several widely used models and state-of-the-art models. The experimental results indicate that the proposed model weakens the effect of outliers, yields high forecasting accuracy with mean square error = 0.35, and outperforms the baselines up to 8.5% on three datasets.

Performance enhancement of short-term wind speed forecasting model using Realtime data.

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.

The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network

This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities.

Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph

Accurate wind speed forecasting can help ensure power-system stability. Many previous studies often neglect spatio-temporal dependence. Therefore, effectively modeling the complex and dynamic spatio-temporal correlations (STCs) between spatially distributed wind speeds and extracting informative spatio-temporal features is very important for boosting forecast accuracy. This study proposes a novel sparse and dynamic graph-based spatio-temporal wind speed forecasting method with local–global features (LGFs). First, a dynamic STC modeling block is designed to learn the dynamic STC degree based on the similarity of wind temporal characteristics. To reduce computational costs, a threshold is set to select the most highly correlated neighboring sites, resulting in a sparse graph. Then, a parallel-structured LGF extraction block including a local feature extraction module and a global feature extraction module is developed. It can capture local features for a single site and global features representing spatio-temporal dependence among neighbor sites according to the obtained graph. The obtained features are fused into the comprehensive LGFs. Finally, accurate wind speed forecasts for multiple sites are generated simultaneously. The proposed model is tested using numerous benchmark models, including temporal, spatio-temporal, static graph-based, and complete graph-based models. The results show that it can effectively learn dynamic STCs and attain the highest accuracy.

A novel wind model downscaling with statistical regression and forecast for the cleaner energy

This study proposes an innovative statistical downscaling approach for meteorological wind models, enhancing cleaner energy applications. This method efficiently and swiftly incorporates topographic and meteorological data into wind park modelling by integrating regression-diagnosis-feature selection with statistical regression and forecasting techniques. The novel method in a de facto reduce predictive errors by approximately an average of 23.21%, lower errors downscaling and forecasting by 5.94% compared to existing methods, and optimise the balance between computational efficiency and forecasting accuracy. In an Arctic wind park case study, we systematically analysed meteorologically simulated wind speeds at various geographic points surrounding the park. The method enabled effective meteorological statistical mapping and accurate wind speed forecasting. Additionally, using the Granger causality test, the model identified a significant influence of surrounding terrain features on wind patterns. This research contributes by 1. introducing a novel downscaling technique surpassing current methods, 2. providing a resource-efficient approach for managing wind energy in complex terrains, and 3. offering a versatile model applicable to broader scenarios. The work marks an advancement in the field, promoting more reliable and efficient wind power and highlighting the importance of statistical downscaling in meteorological contexts for cleaner applications.

Tuning data preprocessing techniques for improved wind speed prediction

Accurate wind speed forecasting is crucial for efficiently integrating of wind power into the electrical grid and ensuring a stable power supply. However, wind speed is inherently noisy and unpredictable, making it challenging to forecast accurately. This study investigates the effects of hyperparameters of Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA) on the accuracy of wind speed forecasting using various prediction models. Our proposed method focuses on the optimisation of hyperparameters within the existing models, suggesting that significant untapped potential remains. Our study examines a wide range of wavelet function orders for DWT and varying trend ratio parameter for SSA, and evaluates their impact on the prediction accuracy using real data from thirteen locations in Jordan. Particularly, our investigation reveals that high-order Daubechies wavelets in DWT outperform low-order wavelets. The study also illustrates that optimal hyperparameters must be modified when changing the prediction model and the combination of DWT and SSA enhances prediction performance when the trend ratio is set to 90%. Our results demonstrate that fine-tuning data preprocessing techniques is essential for accurate wind speed prediction since hyperparameter tuning results in greater improvements in prediction accuracy than sophisticated prediction models alone. Our findings underscore the importance of leveraging data preprocessing techniques and hyperparameter tuning for accurate wind speed forecasting.

Wind speed prediction using non-gaussian model based on Kumaraswamy distribution

ABSTRACT Wind power is a clean source of energy that not only helps in meeting the growing electricity demand but can also play a profound role in transforming the global energy distribution to mitigate the impact of climate change. Accurate forecasting of wind speed is a crucial factor of power system management. The present endeavor focuses on the development of a non-gaussian framework, Kumaraswamy seasonal autoregressive moving average (KSARMA), to model and forecast the wind speed data of 12 locations across the Indian subcontinent. The monthly wind speed dataset during the period 2003 to 2019 has been utilized to perform the analysis. Diagnostic analysis, Akaike’s information criterion (AIC), and conditional maximum likelihood estimator have been used to select the best-fit model for each location. The correlation coefficient between observed and predicted values for all the locations ranges from 0.77 to 0.93. The root mean squared error (RMSE) was found to lie in 0.04 to 0.16 whereas the mean absolute percentage error (MAPE) was observed in 0.06 to 0.2. A comparison of the proposed KSARMA model with the recently developed beta seasonal autoregressive moving average (βSARMA) and Kumaraswamy autoregressive moving average (KARMA) models has also been facilitated. Error estimates such as root mean squared error (RMSE) and mean absolute percentage error (MAPE) reveal that the KSARMA model outperforms the βSARMA and KARMA models except at the Kanyakumari location.

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

At present, wind power is one of the most promising clean energy sources. Due to its high variability, accurate wind speed forecasting is an essential part of the wind power industry. This paper proposes a comprehensive forecasting system consisting of three parts: Firstly, the novel SC-EWT wind speed signal reconstruction system proposed in this article can effectively extract information from wind speed signals. Secondly, the QBGA-GRU model is used for signal reconstruction prediction, where QBGA is an improved genetic algorithm proposed in this article that can improve prediction accuracy. Finally, the IPSO-BLS algorithm is used to quickly analyze and predict prediction errors and further correct the predicted results. The system can provide users with adaptive signal decomposition, high-precision deep learning prediction and error analysis functions to achieve efficient and fast short-term wind speed forecasting, which can be used in the fields of wind power integration deployment and wind turbine early warning. The experiments show that the proposed prediction strategy has good predictive ability for wind speed samples in two regions, and its error is smaller than that of commonly used intelligent algorithms. Compared with other models, the proposed comprehensive prediction strategy has better predictive performance and smaller prediction errors than other combination models.

A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm

Accurate wind speed forecasting is capable of increasing the stability of wind power system. Notably, there are numerous factors affecting wind speed, thus causing wind speed forecasting to be difficult. To address the above-mentioned challenge, a novel hybrid model integrating genetic algorithm (GA), variational mode decomposition (VMD), improved dung beetle optimization algorithm (IDBO), and Bidirectional long short-term memory network based on attention mechanism (BiLSTM-A) is proposed in this study to achieve satisfactory forecasting performance. In the proposed model, GA is adopted to optimize the VMD to eliminate noise and extract original series attributes. And the IDBO is adopted for hyperparameters selection for the BiLSTM-A. The proposed GA-VMD-IDBO-BiLSTM-A is compared with nine established comparable models, with the aim of verifying its forecasting performance. A series of experiments on four 1-hour real wind series in Stratford are performed to assess the performance of the model. The MAPE of the four datasets forecasting results reached 1.4%, 2.4%, 3.5%, 2.4%. As indicated by the experimental results, GA-VMD can better process the data and improve the forecasting accuracy. IDBO can optimize the parameters of BiLSTM model and improve the forecasting performance. The dual-optimization wind speed forecasting model can obtain high accuracy and strong stability.

Robust multi-step wind speed forecasting based on a graph-based data reconstruction deep learning method

Against global warming, wind energy has increasingly become a stable form of power supply. Accurate prediction of wind speed is crucial for turbine control and wind farm dispatch, contributing to stable and continued wind energy utilization. However, it is very difficult to accomplish satisfactory wind speed forecasting, especially multi-step forecasting, due to the stochastic, random and volatile characteristic of wind speed series with complex fluctuations. This study is elaborated to propose a novel method based on “data graph” reconstruction and long short term memory (LSTM) network, to achieve accurate and robust multi-step wind speed forecasting. To obtain implicit correlations between wind speed series, the time series data are reconstructed into matrices like “data graphs”. To achieve better computing efficiency and forecasting accuracy, the convolutional neural network (CNN) is adopted to extract the features in the “data graphs”. Then the extracted data graph features are imported into the bidirectional-LSTM (bi-LSTM) network module, which takes the input in forward and backward directions, respectively, to extract more temporal information. For adequate performance assessment, experiments are carried out on data sets of an actual wind farm located in a mountainous region in China. The results show that the proposed method outperforms classic meta-model based and hybrid-model based methods in accuracy, stability and computing efficiency. The results reveal that the data graph reconstruction combined with the CNN is able to extract the hidden features of the wind speed time series data. The proposed CNN-bi-LSTM method effectively improve the multi-step wind speed prediction accuracy.

Ten-Meter Wind Speed Forecast Correction in Southwest China Based on U-Net Neural Network

Accurate forecasting of wind speed holds significant importance for the economic and social development of humanity. However, existing numerical weather predictions have certain inaccuracies due to various reasons. Therefore, it is highly necessary to perform statistical post-processing on forecasted results. However, traditional linear statistical post-processing methods possess inherent limitations. Hence, in this study, we employed two deep learning methods, namely the convolutional neural network (CNN) and the U-Net neural network, to calibrate the forecast of the Global Ensemble Forecast System (GEFS) in predicting 10-m surface wind speed in Southwest China with a forecast lead time of one to seven days. Two traditional linear statistical post-processing methods, the decaying average method (DAM) and unary linear regression (ULR), are conducted in parallel for comparison. Results show that original GEFS forecasts yield poorer wind speed forecasting performance in the western and eastern Sichuan provinces, the eastern Yunnan province, and within the Guizhou province. All four methods provided certain correction effects on the GEFS wind speed forecasts in the study area, with U-Net demonstrating the best correction performance. After correction using the U-Net, for a 1-day forecast lead time, the proportion of the 10-m U-component of wind with errors less than 0.5 m/s has increased by 46% compared to GEFS. Similarly, for the 10-m V-component of wind, the proportion of errors less than 0.5 m/s has increased by 50% compared to GEFS. Furthermore, we employed the mean square error-based error decomposition method to further diagnose the sources of forecast errors for different prediction models and reveal their calibration capabilities for different error sources. The results indicate that DAM and ULR perform best in correcting the Bias2, while the correction effects of all methods were variable for the distribution with the forecast lead time. U-Net demonstrated the best correction performance for the sequence.

Rolling decomposition method in fusion with echo state network for wind speed forecasting

Accurate wind speed forecasting is beneficial to ensure the safe and stable operation of power systems, improve economic benefits, and promote the healthy development of the wind power industry. This study develops a novel hybrid model called VMD-ESN-STO combining the rolling variational mode decomposition (VMD), echo state network (ESN), and subseries to original series (STO) structure for wind speed forecasting. In this model, the rolling method is not only used in decomposition, but also in training and forecasting. The rolling VMD is used to decompose the original wind speed series into several subseries according to the rolling schema, ESN is used to forecast, and STO structure determines the input and output of the forecasting model. Four wind speed datasets are utilized for wind speed forecasting experiments to validate the applicability and accuracy of the developed model. Mean absolute percentage errors of VMD-ESN-STO in the four datasets are 4.4511%, 2.4451%, 4.1400%, and 2.6178%, respectively, which are far less than the errors for the six comparative models. The developed VMD-ESN-STO is an appropriate tool for wind speed forecasting due to its superior forecasting performance.

Short-term wind speed prediction based on combinatorial prediction model

Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU), Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction models are established to learn features from each subsequence and superimpose the prediction results of subsequences. Finally, Particle Swarm Optimization (PSO) is applied to the results of multiple prediction models to assign weights, combined with weight superimposing sequences to achieve higher accuracy and more robust wind speed prediction. Simulation analysis using data from St. Thomas, Virgin Islands wind measurement station to validate the validity of the combined prediction model. The experimental simulation results show that the model proposed in this paper has a good result on increasing wind speed prediction accuracy.