Wind Power Data Research Articles

Medium-term wind power prediction based on LSTM classification aided Pelt-Neuralprophet HHO-SVM

The precision of wind power prediction plays a vital role in ensuring the stable operation of wind power systems. To elevate this accuracy and enhance the real-time performance, this paper proposes a hybrid Support Vector Machine (SVM) method, with using the Long Short-Term Memory Network (LSTM) to categorize the wind data based on the statistical features and feedback to the trained SVM model. The hybrid Harris Hawk Optimization (HHO) SVM method adopts the Neuralprophet algorithm to model the seasonality of wind power data and then uses the Pelt technique to the LSTM aided Pelt-Neuralprophet HHO-SVM (i.e., PN-HHO-SVM) scheme, which can utilize the seasonal fluctuations inherent in wind power data. The Neuralprophet algorithm is employed to formulate the seasonal regression model of wind power data. Then, the Pelt technique is used to process the modeled data to locate the change points, so as to classify the time series with similar statistical properties. Furthermore, to tune the SVM hyperparameters for each identified cluster, the HHO algorithm is adopted. Consequently, the LSTM aided PN-HHO-SVM is achieved. The real data sourced from the National Renewable Energy Laboratory (NREL) are used for validation case studies, demonstrating the superiorities of the prediction performance, especially in distinguishing and discriminating the seasonal dynamics of wind power data characters.

A novel chaotic time series wind power point and interval prediction method based on data denoising strategy and improved coati optimization algorithm

Wind power prediction plays a pivotal role in increasing the power grid stability and mitigating market transaction risks. To enhance the prediction accuracy of wind power, this study presents a novel chaotic time series wind power point and interval prediction approach, focusing on data processing, as well as an improved coati optimization algorithm. First, the improved wavelet threshold denoising (IWTD) technique is constructed to eliminate the noise from the original wind power data. Then, the maximal information coefficient (MIC) is adopted to determine the optimal inputs of the prediction model. Subsequently, the improved coati optimization algorithm (ICOA) is developed to optimize the hyperparameters of the bidirectional long short-term memory (BiLSTM), deep belief network (DBN), and gated recurrent unit (GRU) models, along with the linear weight coefficients of the combined model, thereby enhancing the point prediction accuracy. Finally, kernel density estimation (KDE) is employed to obtain interval predictions for wind power with different confidence levels. Results show that the proposed prediction method effectively improves the prediction accuracy compared with other popular prediction models.

A novel wind power forecast diffusion model based on prior knowledge

AbstractTo improve the forecast accuracy of wind power, diffusion model based on prior knowledge (DMPK) is proposed. Different from the traditional diffusion model (DM), where the noise perturbation in the diffusion or generation process is random, the noise added in DMPK is modified aiming to the characteristics of wind power signals. The distribution of wind power forecast errors is not a standard Gaussian. Wind power forecast errors are related to forecast methods, weather conditions, and other factors, containing both random signals and certain regularity. This paper adapts the Gaussian distribution to fit the historical forecast error to represent the prior knowledge of wind power. Then, the sampling distribution is derived from its relationship with the fitted prior distribution to replace the standard Gaussian in DM. Taking the prior knowledge into account during the process of noise sampling, the data in the forward process of DMPK can be guided by the distribution of historical errors for diffusion, while the generated result by the reverse process is more consistent with the actual wind power signal. Finally, the superiority of the proposed method is verified by using the wind power data from two real‐world wind farms.

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.

Using enhanced Variational Modal Decomposition and Dung Beetle Optimization Algorithm optimization-kernel Extreme Learning Machine model to forecast short-term wind power

With the widespread use of clean energy, the forecasting of wind power has become increasingly significant. Extracting time series from sophisticated wind power data and thus improving the prediction accuracy of wind power generation has become the key to short-term forecasting. This paper proposes a Kernel Extreme Learning Machine (KELM) prediction model based on Crested Porcupine Optimizer (CPO), Variational Modal Decomposition (VMD) with Improved Dung Beetle Optimization Algorithm (QHDBO). Firstly, by analyzing the correlation between the variables in the wind power data, the CPO algorithm is used to optimize the modal decomposition number and the penalty parameter of the VMD and extract the time-series information of the wind power series, and smooth it to obtain a number of sub-sequences with strong regularity. Then, the KELM prediction model is constructed for different subsequences, and the kernel parameters and regularization coefficients of KELM are optimized using the QHDBO algorithm. Finally, the predicted values of different subsequences are reconstructed to obtain the final prediction results. The simulation results show that the CVMD-QHDBO-KELM model can effectively improve the wind power prediction performance, and the MAPE values are all below 9 %, and most of the RE values are below 10 %.

Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics

This paper proposes a multi-electrolyzer switching scheduling strategy based on the Pelican Optimization Algorithm (POA) to enhance the efficiency of large-scale wind power hydrogen production systems. To validate the effectiveness of the proposed strategy, we analyzed wind power data from three typical days in northern Hebei, China, with a 2.5 MW wind turbine output. We designed three strategies for comparative analysis: a simple start-stop strategy, a rule-based multi-electrolyzer switching strategy, and a POA-based multi-electrolyzer switching strategy. The study results demonstrate that the POA-based strategy exhibits higher hydrogen production efficiency and system stability under various wind conditions. Particularly, in extreme wind scenarios, this strategy significantly reduces the start-stop cycles of electrolyzers, thereby lowering operational costs and improving overall system performance. The main contribution of this study lies in the novel optimization algorithm and its validation through real-world data, demonstrating its effectiveness in large-scale wind power hydrogen production systems. Our findings provide new insights for enhancing the utilization of renewable energy and the economics of hydrogen production systems.

Long-term wind power forecasting with series decomposition and spatio-temporal graph neural network

ABSTRACT The stable operation of the power grid requires accurate predictions of wind power generation and the stabilization of its fluctuations through the integration of other energy sources. An increasing number of deep learning methods are now being employed in the field. However, due to the instability of wind power data, existing methods struggle to uncover deep spatiotemporal dependencies. We propose a novel method named SD-STGNN (Series Decomposition and Spatio-Temporal Graph Neural Network). SD-STGNN first decomposes unstable wind power data into seasonal and trend components. For the seasonal data reflecting short-term fluctuation patterns, we employ a Gated Temporal Convolutional Network and Graph Convolutional Network to capture spatiotemporal relationships. Additionally, for trend data reflecting long-term fluctuation patterns, we introduce a Temporal-Feature Enhancement module, utilizing Multi-Layer Perceptrons to extract deep information along both temporal and feature dimensions. Extensive experiments were conducted on the SDWPF public dataset. Compared to existing state-of-the-art baseline methods, our proposed SD-STGNN model achieves a notable average reduction in Mean Absolute Error by approximately 6.26%, in Root Mean Squared Error by 7.55%, and in Mean Absolute Percentage Error by 2.65%. Additionally, there is an average improvement of about 4.74% in the coefficient of determination.

Prediction of wind power ramp events via a self-attention based deep learning approach

With the increasing popularity of wind power generation, the importance of accurate wind power ramp events (WPRE) prediction has been further emphasized. At present, WPRE prediction model based on deep learning shows superior performance, which does not need to fully grasp the operation of wind power generation, but ignores the interdependence mechanism of prediction error along the input characteristics of the neural network. This paper develops a self-attention (SAM) and wavelet transform (WT) based hybrid 1DCNN (one-dimensional convolutional neural network) and LSTM (long short term memory networks) for WPRE forecasting. In the proposed model, WT effectively separates the middle and high frequency noise signals in wind power and SAM can redistribute the neural weights in 1DCNN-LSTM, then 1DCNN-LSTM further extracts the space-time information of effective wind power. Finally, the performance of the forecast model proposed in this paper is tested by using the wind power data from the Belgian ELIA website. The experimental results show that, compared with the other six competition models, the proposed model effectively suppresses the influence of randomness, volatility and uncertainty of wind power on the recognition of ramp events. Furthermore, through quantitative analysis, this paper explains the reason of SAM is superior to traditional AM in terms of their application object and weight control range in the proposed model.

Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into the power grid. This study presents an effective deep-learning approach that improves short-term wind power forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with a self-attention mechanism applied in both the encoder and decoder. This empowers the model to leverage VAE's strengths in time-series modeling and nonlinear approximation while focusing on the most relevant features within the wind power data. The effectiveness of this approach is evaluated through a comprehensive comparison with eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional LSTMs (ConvLSTMs), Gated Recurrent Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), and vanilla VAEs. Real-world data from five wind turbines in France and Turkey is used for the evaluation. Five statistical metrics are employed to quantitatively assess the prediction performance of each method. The results indicate that the SA-VAE model consistently outperformed other models, achieving the highest average R2 value of 0.992, demonstrating its superior predictive capability compared to existing techniques.

A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.

System Optimization and Operating Strategy of Single-Stage Air Source Heat Pump with Thermal Storage to Reduce Wind Power Curtailment

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction

Wind energy is becoming increasingly competitive, Accurate and reliable multi-engine wind power forecasts can reduce power system operating costs and improve wind power consumption capacity. Existing research on wind power forecasting has neglected the importance of interval forecasting using clusters of wind farms to capture spatial characteristics and the objective selection of forecasting sub-learners, leading to increased uncertainty and risk in system operation. This paper proposes a new “decomposition-aggregation-multi-model parallel prediction” method. The data set is pre-processed by a decomposition-aggregation strategy and spatial feature extraction, and then a Stacking model with multiple parallel sub-learners selected by bootstrap method is used for point and interval forecasting. Experiments and discussions are conducted based on 15-min resolution wind power data from a cluster dataset of a wind farm in northwest China. The experimental results indicate that the method achieves higher accuracy and reliability in both point prediction and interval prediction than other comparative models, with a root mean square error value of 7.47 and an average F value of 1.572, which can provide a reliable reference for power generation planning from wind farm clusters.

A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data Decomposition

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), permutation entropy (PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine (RELM), multi-head attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components as follows: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high-, mid-, and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Beetle Optimizer (DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. The empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM, and MHA-BiGRU. In comparison with other benchmark models, the proposed model exhibits a reduction in Root Mean Squared Error (RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Sample-Based Continuous Approximate Method for Constructing Interval Neural Network.

In safety-critical engineering applications, such as robust prediction against adversarial noise, it is necessary to quantify neural networks' uncertainty. Interval neural networks (INNs) are effective models for uncertainty quantification, giving an interval of predictions instead of a single value for a corresponding input. This article formulates the problem of training an INN as a chance-constrained optimization problem. The optimal solution of the formulated chance-constrained optimization naturally forms an INN that gives the tightest interval of predictions with a required confidence level. Since the chance-constrained optimization problem is intractable, a sample-based continuous approximate method is used to obtain approximate solutions to the chance-constrained optimization problem. We prove the uniform convergence of the approximation, showing that it gives the optimal INN consistently with the original ones. Additionally, we investigate the reliability of the approximation with finite samples, giving the probability bound for violation with finite samples. Through a numerical example and an application case study of anomaly detection in wind power data, we evaluate the effectiveness of the proposed INN against existing approaches, including Bayesian neural networks, highlighting its capability to significantly improve the performance of applying INNs for regression and unsupervised anomaly detection.

Deep Learning Wind Power Prediction Model Based on Attention Mechanism-Based Convolutional Neural Network and Gated Recurrent Unit Neural Network

Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN–GRU–Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model’s performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil’s [Formula: see text], TIC and SPL) and the Diebold–Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model’s superior accuracy and efficiency in predicting wind power.

Mitigation of Performance Degradation of PEM Water Electrolyzers by Power Distribution Control of Fluctuating Renewable Energy

Durability of a PEM electrolysis cell against voltage fluctuation prepared from actual wind power data was analyzed. Two fluctuation patterns were prepared; the pattern A prepared by scaling the wind power data and the pattern B prepared by applying the power distribution control (PDC), that is, converting the pattern A into an alternative sequence of a high-operating rate pattern and a low-operating rate pattern. During 240-h durability tests, the current density continuously decreased for the pattern A, while no distinguishable decrease was observed for the pattern B except the initial 30 h. It was found that the performance degradation is mostly reversible and attributed to increase in the diffusion overpotential consistently with the previous studies. Our results suggest that the PDC suppresses reversible degradation by avoiding gas stagnation at the anode surface, which is important for mitigating irreversible degradation during long-term operation.

Robust optimal power flow considering uncertainty in wind power probability distribution

This paper proposes an optimal power flow model that takes into account the uncertainty in the probability distribution of wind power. The model can schedule controllable generators under any possible distribution of wind power to ensure the safe and economic operation of the system. Firstly, considering the incompleteness of historical wind power data, the paper models the uncertainty of wind power using second-order moments of probability distribution and their fluctuation intervals. Subsequently, a robust optimal power flow model based on probability distribution model and joint chance constraints is established. The Lagrangian duality theorem is then employed to eliminate random variables from the optimization model, transforming the uncertainty model into a deterministic linear matrix inequality problem. Finally, a convex optimization algorithm is used to solve the deterministic problem, and the results are compared with traditional chance-constrained optimal power flow model. The feasibility and effectiveness of the proposed method are validated through case study simulations.

A combined model based on POA-VMD secondary decomposition and LSTM for ultra-short-term wind power forecasting

As the application of wind power expands, precise prediction of wind energy becomes essential for the effective plan and reliable functioning in the realm of the power system. Aiming to enhance wind power utilization efficiency and minimize error relating to ultra-short-term wind power forecasting, a novel model grounded in sliding time window, Pelican optimization algorithm-variational mode decomposition (POA-VMD) secondary decomposition, sample entropy calculation, sequence reconstruction, and long short-term memory (LSTM) prediction is introduced in this paper. First, in the training set, the sliding time window technique is employed to identify the optimal parameters for the forecasting algorithm, aiming to closely replicate the actual forecasting performance. Subsequently, the VMD algorithm is enhanced through optimization with the POA. This involves utilizing POA to dynamically ascertain the optimal parameters [k, α] for VMD, allowing for an adaptive decomposition of the raw wind power data sequence and effectively diminishing data noise. After calculating each modal's sample entropy, the modal with the highest sample entropy is further decomposed using POA-VMD. The decomposed sequence is predicted using LSTM to get the final prediction. The experiment ultimately demonstrated that the introduced model markedly improves the accuracy of forecasting. By adding POA-VMD secondary decomposition residuals, the prediction errors, as measured by mean absolute error, root mean square error, and mean absolute percentage error, are decreased by 52.03%, 30.34%, and 39.87%, respectively, and coefficient of determination (R2) is increased by 7.75%.

Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.