Short-term Wind Power Prediction Research Articles

The short-term wind power prediction based on a multi-layer stacked model of BO-CNN-BiGRU-SA

Wind power generation is influenced by various meteorological factors, exhibiting significant volatility and unpredictability. This variability presents considerable challenges for accurate wind power forecasting. In this study, we propose an innovative method for short-term wind power prediction that integrates a Bayesian-optimized Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-Attention Mechanism (SA) within a multi-layer architecture. Initially, we preprocess features using Pearson correlation analysis and input them into the CNN to investigate complex nonlinear spatial relationships among multiple feature variables and the current load. Subsequently, the BiGRU captures long-term dependencies from both forward and backward time sequences. Finally, we implement the Self-Attention Mechanism to weigh the features and generate the predicted wind power. We optimize the model's numerous hyperparameters utilizing a Bayesian algorithm. Through comparative ablation experiments with varying time segment lengths on wind farm datasets from four regions, our method significantly outperforms 11 models, including Long Short-Term Memory (LSTM), and surpasses several state-of-the-art (SOTA) prediction models, such as iTransformer, PatchTST, Non-stationary Transformers, TSMixer, and DLinear. The highest coefficient of determination (R²) achieved was 0.981, with the Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) decreasing by 11.22% to 62.04% compared to other models. The results demonstrate the predictive accuracy and generalization performance of our proposed model.

A study of short-term wind power segmentation forecasting method considering weather on ramp segments

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research.

MC-VMD-CNN-BiLSTM short-term wind power prediction considering rolling error correction

Abstract Wind energy is a clean and renewable source that has the potential to alleviate the global fossil fuel crisis and environmental pollution by generating electricity. However, accurately predicting wind energy output remains challenging due to its inherent uncertainty. To enhance the accuracy of wind power prediction, a short-term wind power forecasting method for power systems, MC-VMD-CNN-BiLSTM, is proposed, which considers error rolling correction. The method begins with feature selection and outlier handling using the quadrature method. Then, wind power data is decomposed into multiple sub-sequences using the Variational Mode Decomposition (VMD) technique to reduce the raw volatility of wind power. Then, a Convolutional Neural Network (CNN) followed by a Bidirectional Long Short-Term Memory (BiLSTM) model is used for wind power prediction. Finally, the proposed method utilizes the Monte Carlo method for rolling error correction by using known errors from previous time frames to correct subsequent predictions. The MC-VMD-CNN-BiLSTM proposed in this paper considering error rolling correction is compared with ELM, SVM, PSO-BP and ARIMA models through an example analysis of the data of a city, and the proposed model in this paper reduces 61.78%, 50.35%, 62.30% and 73.05% in the NRMSE index in the spring as an example, respectively. The results show that the prediction model proposed in this paper has higher prediction accuracy compared with the traditional prediction model.

BiLSTM-InceptionV3-Transformer-fully-connected model for short-term wind power forecasting

Accurate short-term wind power prediction can help grid managers optimize the transmission and storage of electricity from wind farms, avoiding energy wastage and unnecessary operating costs, and ensuring the stability and reliability of power supply. However, stochastic nature and uncertainty data pose challenges for accurate wind power prediction models. This study combines complementary ensemble empirical mode decomposition (CEEMD), discrete wavelet transform (DWT), bidirectional long short-term memory (BiLSTM), InceptionV3, Transformer, and fully connected neural networks for wind power prediction. The proposed prediction model can fully capture the nonlinear and dynamic characteristics of the wind speed data that are decomposed into intrinsic modal functions by CEEMD. Simultaneously, the prediction model can capture the local characteristics and cyclic variations of the data at different time scales based on fifth-order DWT. A dataset spanning 378 days from Natal is employed and the proposed model is compared with 19 other networks (e.g., ResNet, GoogleNet, SqueezeNet) and 15 other traditional regression algorithms (e.g., Gaussian process regression, least-angle regression, and nearest neighbors regression). The experimental results show that the designed BiLSTM-InceptionV3-Transformer-fully-connected model (BITFM) can reduce the mean absolute error by at least 38.75 % and the root mean squared error by at least 33.33 % compared with 19 other networks. Compared with 15 traditional regression prediction models, BITFM can reduce the mean absolute error by at least 57.41 % and the root mean squared error by at least 53.95 %. To further validate the prediction performance of BITFM, this study conducts tests employing data from the Santa Vitoria do Palmar. The experimental results indicate that BITFM consistently achieved the best performance metrics.

A short-term wind power prediction method via self-adaptive adjacency matrix and spatiotemporal graph neural networks

Graph neural networks (GNN) recently have been successfully applied in wind power prediction by employing geo-information to construct the graphs that serve as the GNN-based prediction model. However, these graphs cannot maintain the latent correlation and attribute of the wind turbines, leading to inferior performance. This research proposes an optimizing wind power prediction model through attention mechanism and spatiotemporal graph neural networks. Initially, the spectral clustering and a self-adjacency matrix to construct the graph nodes and edges. Subsequently, the proposed ASTGNN combined from graph convolutional networks and a gated recurrent unit with an attention mechanism is designed to act as the prediction model. A comprehensive set of experiments has been carried out and the proposed method not only improved prediction accuracy but also enhanced computational efficiency. According to the results obtained, the developed model demonstrated an improvement that is up to 13 % in terms of RMSE and 6.7 % in terms of computation time compared to the latest models.

A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network

The uncertainty of the wind process leads to the randomness of its regional propagation, and the spatial correlation between nearby wind farms also shows a dynamic change tendency under the influence of wind direction. To consider the influence of dynamic spatial correlation on wind power prediction modeling, a short-term wind power prediction method based on a dynamic graph neural network is proposed. First, the topology graph of the wind farm cluster was established to represent the correlation of wind farms based on graph theory. Then, a dynamic spatiotemporal graph neural network was constructed to adapt graph topology by node embedding, which can explore the changing characteristics of spatial correlation among wind farms. Finally, we proposed a decoupling error model that can quantify the proportion of errors caused by the modeling process, which can assist in evaluating the performance of predictive models. We conducted experiments using data provided by the wind farm cluster in Inner Mongolia, and the average normalized Root Mean Square Error for the 12 wind farms was 0.1424, which verified the effectiveness of the proposed model.

Short-term wind power prediction method based on multivariate signal decomposition and RIME optimization algorithm

Accurate characterization of the dynamics of wind power systems under highly unstable conditions is essential for short-term wind power forecasting. Multivariate factors, such as wind speed, wind direction and climatic factors at different heights, as well as the relationships between them, need to be fully considered. To this end, after the Pearson correlation coefficient is used to identify the main influencing variables, the empirical modal decomposition (MEMD) method is utilized to decompose the data in order to capture the deep coupling relationship and the spatio-temporal pattern of change among the variables. Combined with a bidirectional long and short-term memory network (BiLSTM) optimized through the RIME algorithm, a short-term wind power prediction model was constructed. Data analysis of two wind farms in Xinjiang and Gansu, China, shows that the model has higher prediction accuracy compared to the baseline model. The effectiveness of RIME and MEMD is verified through component ablation experiments and comparison experiments with other optimization algorithms and signal decomposition algorithms. To cope with large-scale data, dimensionality reduction of IMF features using Elastic Net (EN) is explored to reduce computational requirements and maintain good prediction performance. The method provides strong support for power system scheduling and efficient utilization of renewable energy.

Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction

The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid. In this work, a deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet. The dataset was collected in Natal. The six algorithms employed predicted the value of wind power for the coming day. Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics. The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error.

Wind power prediction method based on Attention-LSTM

Abstract Under the background of realizing the goal of “double carbon”, precise short-term wind power prediction can offer a scientific foundation for the effective integration of wind energy into the grid and the secure and steady functioning of the electricity system. However, the unpredictability of weather conditions causes wind power to be volatile, which makes prediction more difficult. Aiming at this problem, a short-term wind power prediction technique that combines LSTM neural network and attention is suggested, which can make the predictive model more effective in processing the inputs of relevant influencing factors. The outcomes demonstrate the higher accuracy of the Attention-LSTM prediction method., with the mean value of RSME of 6.12, the mean value of MAE of 4.51, and the mean value of MAPE of 18%, when the prediction model is trained with real data.

Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models

Accurate wind power prediction helps to fully utilize wind energy and improve the stability of the power grid. However, existing studies mostly analyze key wind power-related features equally without distinguishing the importance of different features. In addition, single models have limitations in fully extracting input feature information and capturing the time-dependent relationships of feature sequences, posing significant challenges to wind power prediction. To solve these problems, this paper presents a wind power forecasting approach that combines feature weighting and a combination model. Firstly, we use the attention mechanism to learn the weights of different input features, highlighting the more important features. Secondly, a Multi-Convolutional Neural Network (MCNN) with different convolutional kernels is employed to extract feature information comprehensively. Next, the extracted feature information is input into a Stacked BiLSTM (SBiLSTM) network to capture the temporal dependencies of the feature sequence. Finally, the prediction results are obtained. This article conducted four comparative experiments using measured data from wind farms. The experimental results demonstrate that the model has significant advantages; compared to the CNN-BiLSTM model, the mean absolute error, mean squared error, and root mean squared error of multi-step prediction at different prediction time resolutions are reduced by 35.59%, 59.84%, and 36.77% on average, respectively, and the coefficient of determination is increased by 1.35% on average.

Short-Term Wind Power Prediction Based on Encoder-Decoder Network and Multi-Point Focused Linear Attention Mechanism.

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder-decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM-Attention-LSTM, LSTM-Self_Attention-LSTM, and CNN-LSTM-Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R2, thereby validating its predictive performance.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

Short-term wind power prediction and uncertainty analysis based on VDM-TCN and EM-GMM

Due to the fluctuating and intermittent nature of wind energy, its prediction is uncertain. Hence, this paper suggests a method for predicting wind power in the short term and analyzing uncertainty using the VDM-TCN approach. This method first uses Variational Mode Decomposition (VDM) to process the data, and then utilizes the temporal characteristics of Temporal Convolutional Neural Network (TCN) to learn and predict the dataset after VDM processing. Through comparative experiments, we found that VDM-TCN performs the best in short-term wind power prediction. In wind power prediction for 4-h and 24-h horizons, the RMSE errors were 1.499% and 4.4518% respectively, demonstrating the superiority of VDM-TCN. Meanwhile, the Gaussian Mixture Model (GMM) can effectively quantify the uncertainty of wind power generation at different time scales.

Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm

Short-Term Wind Power Prediction Based on a Modified Stacking Ensemble Learning Algorithm

Short-term wind power prediction based on ICEEMDAN-Correlation reconstruction and BWO-BiLSTM

Short-term wind power prediction based on ICEEMDAN-Correlation reconstruction and BWO-BiLSTM

Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning

Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning

Short-term wind power prediction based on improved sparrow search algorithm optimized long short-term memory with peephole connections

Accurate short-term wind power prediction is of great significance for the scheduling and management of wind farms. This paper proposes a model for short-term wind power prediction. Firstly, on the basis of traditional long short-term memory network, the peephole connections is added. The improved long short-term memory network is more stable compared to traditional long short-term memory neural networks and is suitable for regression prediction. Secondly, chaotic mapping, adaptive weights, Cauchy mutation, and opposition-based learning strategies are introduced to improve the sparrow search algorithm, and applied to optimize the four hyper-parameters of the long short-term memory network, greatly improving the prediction accuracy of the network. The effectiveness of the model is validated using two short-term wind power datasets with sampling times of 10 and 30 minutes respectively, combined with some fitting curves and performance indicators. The comparison results indicate that the proposed short-term wind power prediction model has high prediction accuracy.

Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks

Wind energy is a clean resource widely utilized as a renewable energy source. However, due to its inherent strong volatility and the multitude of influencing factors, it is challenging to accurately predict wind power. To address these issues, an IVMD-LASSO-BiGRU model, comprising Improved Variational Mode Decomposition (IVMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Bidirectional Gated Recurrent Unit (BiGRU), is proposed for forecasting. Firstly, based on the sparse prior knowledge of each component constructed in the variational model by VMD, the optimal decomposition mode number K is determined at the inflexion point where the sparsity index shifts from rising to falling. The original wind power sequence is then decomposed into a series of Intrinsic Mode Functions (IMFs) using VMD with the optimal K value, thereby reducing the volatility of the original sequence. Secondly, LASSO is employed to select key features from meteorological data, historical wind power, and IMFs, thereby reducing the data dimension. Subsequently, BiGRU is utilized to fully extract the temporal features of the input data, establishing the mapping between input and output. Experimental results demonstrate that on three different datasets, the R2 values of the proposed forecasting method reach 0.9872, 0.9917, and 0.9941, respectively. Compared to the traditional BiGRU model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by an average of 57.56% and 58.88%, respectively. Thus, it is evident that the proposed method enhances the accuracy of short-term wind power forecasting, providing a basis for adjusting power generation plans.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.