Short-term Wind Power Prediction Research Articles

Short-term wind power prediction with harmony search algorithm: Belen region

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

Research on the prediction of short-term wind power based on wavelet neural networks

The theory of applying wavelet neural networks to predict the time series of short-term wind power has important practical significance for the construction of emergency response systems in smart grids. Based on obtaining the low-frequency approximate part and high-frequency random part of wind power data through wavelet decomposition and reconstruction, and analyzing the advantages and disadvantages of various models, a short-term wind power prediction model is established. The simulation of the model realizes the complex mapping from the relevant historical wind power to the prediction target. The prediction results obtained from the measured wind power data of a wind farm show that the model has a fast prediction speed and high accuracy. It can effectively improve the prediction accuracy of short-term wind power.

An ensemble method for short-term wind power prediction considering error correction strategy

With a high proportion of renewable energy injected into the power grid, the accurate wind power prediction of wind farms is key to improving power quality and ensuring the stable operation of the power grid. In this paper, an ensemble learning prediction model considering the rolling error correction strategy used for short-term wind power prediction is proposed. Firstly, the ensemble learning prediction model integrating multiple Gradient Boosting Trees (GBDTs) based on Bayesian optimization is established, and the characteristics of prediction errors are analyzed. To further improve the prediction accuracy, a daily rolling error correction strategy based on the Swing Window segmentation method, Spearman correlation coefficient and Quantile Regression is established to obtain the optimal compensation amount. Finally, the corrected short-term wind power prediction results are obtained. The two-year dataset collected from a regional wind farm in China is used as the benchmark test. Different prediction models and error correction strategies are compared, and the effectiveness of the proposed method is comprehensively evaluated. The results from different seasons show that the proposed prediction method has good stability, and the proposed error correction strategy has good generalization ability.

Short-term wind power prediction based on data decomposition and fusion

Due to the randomness of wind power generation, the output power of a wind farm will fluctuate. As a result, the power grid can face the need for increased reserve capacity, increasing scheduling difficulties and wind farm abandonment. An effective way to address these problems is to accurately predict the output power of wind farms. Traditional prediction methods usually make predictions based on wind data obtained at a single height. However, with long prediction periods, prediction errors are relatively large because the wind speed and direction at different heights have spatiotemporal correlations within a wind farm. In the model presented here, the wind power data are first decomposed by a ‘complete ensemble empirical mode decomposition with adaptive noise’ model to obtain modal components with different fluctuation characteristics. Then, the characteristics of wind speed, wind direction, air pressure and other data at different heights are extracted for spatiotemporal feature fusion. Actual measurement data from a wind farm in Chongqing, China are used to verify the feasibility and effectiveness of the proposed method.

Short-run Wind Power Combination Projection Model based on CEEMDAN-TPA-TCN-MRFO

Wind power is playing an increasingly critical role in new energy sources. In order to modify the precision of short-term wind power prediction, this thesis proposes a combined model for short-term wind power prognosticate based on Complete Ensemble Empirical Mode Decomposition of Adaptive Noise (CEEMDAN), Time Pattern Attention mechanism (TPA), Time Convolution Network (TCN) and Multi-strategy Manta Ray Foraging Optimization (MRFO). Firstly, this thesis uses CEEMDAN algorithm decompose the wind power sequence, and obtain the sub-sequence components. Combined with key meteorological variable data, this thesis constructs a training set. Then, it optimizes the convolutional network hyperparameters with MRFO. Finally, the final forecasting value is obtained after reconstructing the forecasting results by predicting sub-sequence components respectively with TPA and TCN. The practical cases reveal that the propound combined forecasting model can better predict the wind power change trend. Compared with other methods, this model can significantly improve the short-term presaging accuracy of wind power.

Hybrid Short-Term Wind Power Prediction Based on Markov Chain

This article proposes a combined prediction method based on the Markov chain to realize precise short-term wind power predictions. First, three chaotic models are proposed for the prediction of chaotic time series, which can master physical principles in wind power processes and guide long-term prediction. Then, considering a mechanism switching between different physical models via a Markov chain, a combined model is constructed. Finally, the industrial data from a Chinese wind farm were taken as a study case, and the results validated the feasibility and superiority of the proposed prediction method.

Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks

Wind power is a rapidly growing source of clean energy. Accurate short-term forecasting of wind power is essential for reliable energy generation. In this study, we propose a novel wind power forecasting approach using spatiotemporal analysis to enhance forecasting performance. First, the wind power time-series data from the target turbine and adjacent neighboring turbines were utilized to form a graph structure using graph neural networks (GNN). The graph structure was used to compute the spatiotemporal correlation between the target turbine and adjacent turbines. Then, the prediction models were trained using a deep residual network (DRN) for short-term wind power prediction. Considering the wind speed, the historic wind power, air density, and historic wind power in adjacent wind turbines within the supervisory control and data acquisition (SCADA) system were utilized. A comparative analysis was performed using conventional machine-learning approaches. Industrial data collected from Hami County, Xinjiang, China, were used for the case study. The computational results validate the superiority of the proposed approach for short-term wind-power forecasting.

Short-Term Prediction of Wind Power Considering the Fusion of Multiple Spatial and Temporal Correlation Features

As the wind power penetration increases, the short-term prediction accuracy of wind power is of great importance for the safe and cost-effective operation of the power grid in which the wind power is integrated. Traditional wind farm power prediction uses numerical weather prediction (NWP) information as an important input but does not consider the correlation characteristics of NWP information from different wind farms. In this study, a convolutional neural network–long short-term memory based short-term prediction model for wind farm clusters is proposed. Additionally, a feature map is established for multiposition NWP information, the spatial correlation of NWP information from different wind farms is fully explored, and the feature map is trained using the spatiotemporal model to obtain the short-term prediction results of wind farm clusters. Finally, as a case study, the operational data of a wind farm cluster in China are analyzed, and the proposed model outperforms traditional short-term prediction methods in terms of prediction accuracy.

Capturing Spatial Influence in Wind Prediction With a Graph Convolutional Neural Network

Nowadays, wind power is playing a significant role in power systems; it is necessary to improve the prediction accuracy, which will help make better use of wind sources. The existing neural network methods, such as recurrent neural network (RNN), have been widely used in wind prediction; however, RNN models only consider the dynamic change of temporal conditions and ignore the spatial correlation. In this work, we combine the graph convolutional neural (GCN) with the gated recurrent unit (GRU) to do prediction on simulated and real wind speed and wind power data sets. The improvements of prediction results by GCN in all wind speed experiments show its ability to capture spatial dependence and improve prediction accuracy. Although the GCN does not perform well in short-term wind power prediction as the change of wind power data is not so smooth due to the limitation of turbine operation, the results of long-term prediction still prove the performance of GCN.

Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition

Short-term wind power prediction has a considerable effect on improving the productivity of wind energy systems and increasing economic benefits. In recently years, various wind velocity predictive models have been designed to raise the prediction effect. However, numerous predictive systems are limited by single type, and many ordinary predictive systems ignore the advantage of optimized parameters and the significance of data preparation, which bring about the lower predictive precision. To fill this gap, in this article, a novel predictive system is come up, which is on the basis of data denoising strategy, statistical predictive systems, artificial intelligence forecasting system and multi-objective optimization strategy. After using the data denoising strategy for denoising, the reconstructed data is used for the forecasting of different sub-systems, to obtain stable forecasting results, multi-objective dragonfly algorithm is used to estimate the weight coefficient of sub-systems. To evaluate the availability of the designed predictive system, five wind velocity datasets from different wind farms are used for the purpose of a case research. According four experiments and four analyses, it can be concluded that the designed combined system has a well predictive effect in short-term wind speed prediction. And it is in favor of grid regulation and operation.

A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism

This paper proposes a CNN-LSTM-LightGBM based short-term wind power prediction method based on the attention mechanism, which contains three main parts: data preprocessing, model training and model prediction. In the data preprocessing stage, the historical environment and historical wind power data are collected, then data cleaning and normalization and other preprocessing on the data are performed; in the model training stage, we first build a CNN-LSTM model (model 1) that includes an attention mechanism. CNN network includes the Conv1D layer, the MaxPooling1D layer and the LSTM network includes the basic LSTM layer, the attention layer, the Dropout layer and the final Dense layer. Secondly, we build the LightGBM model (model 2), using the training set and the validation set for the two models above separately. In the model prediction stage, the trained model 1 and model 2 are used to make parallel predictions on the test set, and the MAPE-RW algorithm is employed to linearly combine model 1 and model 2 to form the final combined prediction model. The proposed prediction method considers various environmental factors including weather, wind speed, wind direction, temperature, pressure, humidity, etc., effectively extracts the local characteristics and time series characteristics of the data, and allocates the feature weights reasonably, thus can realize the accurate prediction of wind power.

Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network

Abstract A short-term wind power prediction method is proposed in this paper with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method using a traditional back-propagation (BP) neural network algorithm, the improved grey wolf optimization (IGWO) algorithm has been adopted to optimize its parameters. The performance of the proposed method has been evaluated by experiments. First, the features of the wind farm are described to show the fundamental information of the experiments. A single turbine with rated power of 1500 kW and power generation coefficient of 2.74 in the wind farm was introduced to show the technical details of the turbines. Original wind power data of the whole farm were preprocessed by using the quartile method to remove the abnormal data points. Then, the retained wind power data were predicted and analysed by using the proposed IGWO–BP algorithm. Analysis of the results proves the practicability and efficiency of the prediction model. Results show that the average accuracy of prediction is ~11% greater than the traditional BP method. In this way, the proposed wind power prediction method can be adopted to improve the accuracy of prediction and to ensure the effective utilization of wind energy. A short-term wind power prediction method is designed and tested with experimental results obtained from a wind farm located in Northeast China. In order to improve the accuracy of the prediction method, the improved grey wolf optimization algorithm has been adopted to optimize its parameters.

A short-term wind power prediction model based on CEEMD and WOA-KELM

Effective short-term wind power prediction is crucial to the optimal dispatching, system stability, and operation cost control of a power system. In order to deal with the intermittent and fluctuating characteristics of the wind power time series signals, a hybrid forecasting model is proposed, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Whale Optimization Algorithm (WOA)-Kernel Extreme Learning Machine (KELM), to predict short-term wind power. Firstly, the non-stationary wind power time series was decomposed into a series of relatively stationary components by CEEMD. Then, the components were used as the training set for the KELM prediction model, in which the initial values and thresholds were optimized by WOA. Finally, the predicted output values of each component were superimposed, to obtain the final prediction of the wind power values. The experimental results show that the proposed prediction method can reduce the complexity of the prediction with a small reconstruction error. Furthermore, performance is greater, in terms of prediction accuracy and stability, with lower computational costs than other benchmark models.

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction, a soft margin multiple kernel learning (MKL) method is proposed. In order to improve the predictive effect of the MKL method for wind power, a kernel slack variable is introduced into each base kernel to solve the objective function. Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected. The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function, thereby achieving an effective yet sparse solution for the MKL method. In order to verify the effectiveness of the proposed method, the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction, and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator (AESO). Compared with the support vector machine (SVM), extreme learning machine (ELM), kernel based extreme learning machine (KELM) methods as well as the SimpleMKL method under the same conditions, the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction, which confirms the effectiveness of the method.

Short-Term Wind Power Prediction Based on Data Reconstruction and Improved Extreme Learning Machine

Short-Term Wind Power Prediction Based on Data Reconstruction and Improved Extreme Learning Machine

Wind Power Prediction Based on a Hybrid Granular Chaotic Time Series Model

For realizing high-accuracy short-term wind power prediction, a hybrid model considering physical features of data is proposed in this paper, with consideration of chaotic analysis and granular computing. First, considering the chaotic features of wind power time series physically, data reconstruction in chaotic phase space is studied to provide a low-dimensional input with more information in modeling. Second, considering that meteorological scenarios of wind development are various, complicated, and uncertain, typical chaotic time series prediction models and wind scenarios are analyzed correspondingly via granular computing (GrC). Finally, through granular rule-based modeling, a hybrid model combining reconstructed wind power data and different models is constructed for short-term wind power prediction. Data from real wind farms is taken for experiments, validating the feasibility and effectiveness of the proposed wind power prediction model.

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

A short-term wind power prediction model based on BiLSTM–CNN–WGAN-GP (LCWGAN-GP) is proposed in this paper, aiming at the problems of instability and low prediction accuracy of short-term wind power prediction. Firstly, the original wind energy data are decomposed into subsequences of natural mode functions with different frequencies by using the variational mode decomposition (VMD) algorithm. The VMD algorithm relies on a decision support system for the decomposition of the data into natural mode functions. Once the decomposition is performed, the nonlinear and dynamic behavior are extracted from each natural mode function. Next, the BiLSTM network is chosen as the generation model of the generative adversarial network (WGAN-GP) to obtain the data distribution characteristics of wind power’s output. The convolutional neural network (CNN) is chosen as the discrimination model, and the semi-supervised regression layer is utilized to design the discrimination model to predict wind power. The minimum–maximum game is formed by the BiLSTM and CNN network models to improve the quality of sample generation and further improve the prediction accuracy. Finally, the actual data of a wind farm in Jiuquan City, Gansu Province, China is taken as an example to prove that the proposed method has superior performance compared with other prediction algorithms.

Comparative study of reformed neural network based short‐term wind power forecasting models

Short-term prediction of wind power plays a vital role in wind power application. In order to improve the accuracy of wind power forecasting, this paper investigates neural network combined forecasting models to forecast the wind power, the data of a real wind farm, the Pacific Wind Farm, is used. In view of the difficulty of predicting the large fluctuations of wind power, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the wind power time series which can reduce the complexity of the forecasting process, and then the intrinsic mode function (IMF) signal is predicted by the BP Neural Network, wavelet neural network (WNN) and long short-term memory (LSTM) neural network respectively, and the final result is obtained through wavelet reconstruction. By comparing with a single model, the combined prediction model has better prediction accuracy and stability, among them, the NMAE predicted by CEEMDAN-GA-BP in January was 4.167%, and the NRMSE was 6.590%. Reformed neural network based short-term wind power forecasting models proposed in here provides very useful information for operation and control of high renewable energy penetrated power systems.

Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function

The seasonality and randomness of wind present a significant challenge to the operation of modern power systems with high penetration of wind generation. An effective short-term wind power prediction model is indispensable to address this challenge. In this paper, we propose a combined model, i.e., a wind power prediction model based on multi-class autoregressive moving average (ARMA). It has a two-layer structure: the first layer classifies the wind power data into multiple classes with the logistic function based classification method; the second layer trains the prediction algorithm in each class. This two-layer structure helps effectively tackle the seasonality and randomness of wind power while at the same time maintaining high training efficiency with moderate model parameters. We interpret the training of the proposed model as a solvable optimization problem. We then adopt an iterative algorithm with a semi-closed-form solution to efficiently solve it. Data samples from open-source projects demonstrate the effectiveness of the proposed model. Through a series of comparisons with other state-of-the-art models, the experimental results confirm that the proposed model improves not only the prediction accuracy, but also the parameter estimation efficiency.

A Short-term Wind Power Prediction Framework based on Two-layer Decomposition and the Combination of Ensemble Model and Deep Network

The time series of wind power is influenced by many external factors, showing strong volatility and randomness. Aiming at the problem of low prediction accuracy of wind power time series, this paper proposes a short-term wind power prediction framework based on two-layer decomposition and the combination of ensemble model and deep network, which is composed of complete ensemble empirical mode decomposition (CEEMD), sample entropy (SE), stacking ensemble, linear regression (LR), variational mode decomposition (VMD), long short term memory (LSTM) and multi-layer perceptron (MLP). Firstly, CEEMD is used to decompose the time series of wind power into different modes and then SE is used for reconstruction. Secondly, different models are applied to predict the different reconstruction components and select the optimal model. Subsequently, VMD is used to decompose the partially decomposed reconstruction components and a combined prediction model of stacking ensemble and LSTM is established. Finally, in order to further improve the prediction accuracy, MLP is applied to correct the error and the corrected error is superimposed with the prediction results and other reconstruction components to obtain the final predicted value. The simulation results show that the accuracy and effectiveness of this model is superior to the traditional model and the prediction accuracy of short-term wind power time series is improved effectively.