A novel wind speed prediction method based on fractal wavelet decomposition explainable gated recurrent unit

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

Accurate prediction of wind speed and wind power is of great significance to the operation, planning, dispatching and control of power system. In order to make full use of the effective information provided by SCADA system and NWP to further improve the prediction accuracy of wind speed and wind power. A short-term wind speed and wind power prediction method based on meteorological correction model is proposed in this paper. Firstly, the meteorological model based on matrix completion algorithm is established to modify the meteorological data. Secondly, the network is trained with the data of meteorological model modification as input and the actual power of fan as output, and the prediction model based on LSTM network is established. Finally, the short-term prediction of wind speed and wind power is completed. The measured data from a wind farm is used for verification. The research results show that the information in multiple data sources can be well used in the proposed method to complete the prediction of wind speed and wind power. And in the future, the waste of wind resources can be effectively reduced, so as to realize the economic and stable operation of the power grid.

The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.

Wind power generated by wind has non-schedule nature due to stochastic nature of meteorological variable. Hence energy business and control of wind power generation requires prediction of wind speed (WS) from few seconds to different time steps in advance. To deal with prediction shortcomings, various WS prediction methods have been used. Predictive data mining offers variety of methods for WS predictions where artificial neural network (ANN) is one of the reliable and accurate methods. It is observed from the result of this study that ANN gives better accuracy in comparison conventional model. The accuracy of WS prediction models is found to be dependent on input parameters and architecture type algorithms utilized. So the selection of most relevant input parameters is important research area in WS predicton field. The objective of the paper is twofold: first extensive review of ANN for wind power and WS prediction is carried out. Discussion and analysis of feature selection using Relief Algorithm (RA) in WS prediction are considered for different Indian sites. RA identify atmospheric pressure, solar radiation and relative humidity are relevant input variables. Based on relevant input variables Cascade ANN model is developed and prediction accuracy is evaluated. It is found that root mean square error (RMSE) for comparison between predicted and measured WS for training and testing wind speed are found to be 1.44 m/s and 1.49 m/s respectively. The developed cascade ANN model can be used to predict wind speed for sites where there are not WS measuring instruments are installed in India.

According to the characteristics of randomness, volatility, and unpredictability of wind speed, this article provides a new wind speed prediction method which includes three modules that are attribute weighting module, intelligent optimization clustering module, and wind speed prediction module based on extreme learning machine. First, the Pearson coefficient values of the attribute matrix elements are calculated and weighted considering the fluctuation characteristics of time series and influences of different weather attributes on the wind speed. Then the fuzzy c-means clustering method optimized by genetic simulated annealing algorithm is carried out on the weighted attribute matrix to cluster. Furthermore, several kinds of wind speed prediction models are built using the extreme learning machine to forecast short-term wind speed. The research on wind speed prediction is carried out by the measured data of wind farm in America (N39.91°, W105.29°). And the results show that the new prediction method of wind speed proposed in this article has higher prediction accuracy.

Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression

The intermittency and uncertainty of wind power may pose danger on the safety on the power system, thus the research of wind speed and wind power prediction methods has been widely concerned. The keys of wind power forecasting are the forecasting model selection and model optimization. In this paper, the least squares support vector machine (LSSVM) is chosen as the wind speed and wind power prediction model and improved ant colony optimization (IACO) algorithm is used to optimize the most important parameters which influence the least squares support vector machine (LSSVM) regression model. In the IACO-LSSVM method, the kernel parameter σ and regularization parameter γ were considered the position vector of ants, the improved pheromone updating method is used to effectively solve the contradiction between expanding search and finding optimal solution. A multi-input–multi-output (MIMO) short-term wind speed and wind power forecasting model is built and applied in a wind farm of Gansu province in order to predict wind speed and wind power. For comparative study, PSO-LSSVM model and SVM model are used for forecasting. Prediction analysis results show that the IACO-LSSVM model can achieve higher prediction accuracy and confirm the effectiveness and feasibility of the method.

Accurately predicting wind speed is crucial for the generation efficiency of offshore wind energy. This paper proposes an ultra-short-term wind speed prediction method using a graph neural network with a multi-head attention mechanism. The methodology aims to effectively explore the spatio-temporal correlations present in offshore wind speed data to enhance the accuracy of wind speed predictions. Initially, the offshore buoys are organized into a graphical network. Subsequently, in order to cluster the nodes with comparable spatio-temporal features, it clusters the nearby nodes around the target node. Then, a multi-head attention mechanism is incorporated to prioritize the interconnections among distinct regions. In the construction of the graph neural network, a star topology structure is formed by connecting additional nodes to the target node at the center. The effectiveness of this methodology is validated and compared to other time series-based approaches through comparative testing. Metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R yielded values of 0.364, 0.239, 0.489, and 0.985, respectively. The empirical findings indicate that graph neural networks utilizing a multi-head attention mechanism exhibit notable benefits in the prediction of offshore wind speed, particularly when confronted with intricate marine meteorological circumstances.

Accurate forecasting of short-term wind speed is a key technology to enable efficient and reliable operation of microgrids with wind generators. Results of wind speed prediction (WSP) methods in the current literature are subject to errors due to the random nature of wind speed and the limited generalization of forecast algorithms. In this paper, a short-term WSP method based on a model of forecast, error correction, wind power generation, support vector machines (SVMs), and prediction algorithms is proposed. In the proposed method, the error model is built to predict the errors of the original predicted wind speed. Subsequently, the predicted errors are incorporated into the original predicted sequence to produce the final forecasts. The final predicted results can be brought to the system operator step by step for use in scheduling strategies. Using SVM and back propagation (BP) prediction algorithm as examples for the basic prediction algorithm, the proposed method is applied to a day-ahead WSP with five daily deliveries and compared with the results by a single SVM or a BP prediction algorithm. The test results demonstrate significant improvement in prediction accuracy using the proposed short-term WSP based on a model of forecast error correction method, independent of the basic prediction algorithm.

Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network

Improving power output wind turbine in micro-grids assisted virtual wind speed prediction

As a typical kind of the Internet of Things, smart grid has attracted a lot of attentions. The power energy management of smart grid is of great importance for energy distribution, system security, and market economics. One of the most important issues is the accurate and stable prediction of wind speed for the optimal operation and management of wind power generations connected to smart grid. In this paper, a novel two-layer nonlinear combination method termed as EEL-ELM is developed for short-term wind speed prediction problems, such as 10-min ahead and 1-h ahead. The first layer is based on extreme learning machine (ELM), Elman neural network (ENN), and long short term memory neural network (LSTM) to separately forecast wind speed by making use of their merits of calculation speed or strong ability in forecasting, and obtain three forecasting results. Then, we propose the second layer by making use of ELM-based nonlinear aggregated mechanism to alleviate the inherent weakness of single method and linear combination. Two real-world case studies, gathered from Inner Mongolia's wind farm in China, are implemented to demonstrate the effectiveness of the proposed EEL-ELM method. By comparing with other eight wind speed prediction methods, the simulation results reveal that EEL-ELM can achieve better forecasting performance according to three evaluation metrics and three statistical tests.

Accurate and stable wind speed prediction is crucial for the safe operation of large-scale wind power grid connections. Existing methods are typically limited to a certain fixed area when learning the information of the wind speed sequence, which cannot make full use of the spatiotemporal correlation of the wind speed sequence. To address this problem, in this paper we propose a new wind speed prediction method based on collaborative filtering against a virtual edge expansion graph structure in which virtual edges enrich the semantics that the graph can express. It is an effective extension of the dataset, connecting wind turbines of different wind farms through virtual edges to ensure that the spatial correlation of wind speed sequences can be effectively learned and utilized. The new collaborative filtering on the graph is reflected in the processing of the wind speed sequence. The wind speed is preprocessed from the perspective of pattern mining to effectively integrate various information, and the k-d tree is used to match the wind speed sequence to achieve the purpose of collaborative filtering. Finally, a model with long short-term memory (LSTM) as the main body is constructed for wind speed prediction. By taking the wind speed of the actual wind farm as the research object, we compare the new approach with four typical wind speed prediction methods. The mean square error is reduced by 16.40%, 11.78%, 9.57%, and 18.36%, respectively, which demonstrates the superiority of the proposed new method.

Accurate wind speed prediction can improve or avoid the impact to grid caused by wind power generation, so a wind speed prediction method is researched based on High Frequency-Empirical Mode Decomposition (HF-EMD) and BP neural network. Firstly, HF-EMD is used to decompose wind speed sequence into a series of subsequences which are smoother than original sequence, then BP neural network is used to establish model and forecast subsequences, finally, the forecasting result of original wind speed sequence is obtained by adding all the forecasting results of subsequences. The research results show: the forecasting accuracy used by HF-EMD-BP neural network is higher than that by BP neural network and EMD-BP neural network, because the problem of mode mixing of EMD can be improved by HF-EMD, thus, the forecasting accuracy for wind speed prediction is improved effectively.

This paper introduces the concept and practice of Neural Network architectures for wind speed prediction in wind farms. The wind speed prediction method has been analyzed by using back propagation network and radial basis function network. Artificial neural network is used to develop suitable architecture for predicting wind speed in wind farms. The key of wind speed prediction is rational selection of forecasting model and effective optimization of model performance. To verify the effectiveness of neural network architecture, simulations were conducted on real time wind data with different heights of wind mill. Due to fluctuation and nonlinearity of wind speed, accurate wind speed prediction plays a major role in the operational control of wind farms. The key advantages of Radial Basis Function Network include higher accuracy, reduction of training time and minimal error. The experimental results show that compared to existing approaches, proposed radial basis function network performs better in terms of minimization of errors.