Accuracy Of Wind Power Prediction Research Articles

A Review of Modern Wind Power Generation Forecasting Technologies

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

With the emergence of various new power systems, accurate wind power prediction plays a critical role in their safety and stability. However, due to the historical wind power data with few samples, it is difficult to ensure the accuracy of power system prediction for new wind farms. At the same time, wind power data show significant uncertainty and fluctuation. To address this issue, it is proposed in this research to build a novel few-sample wind power prediction model based on the least-square generative adversarial network (LSGAN) and quadratic mode decomposition (QMD). Firstly, a small amount of original wind power data are generated to improve the data by least-square generative adversarial network, which solves the error in prediction with limited sample data. Secondly, a quadratic mode decomposition method based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is developed to address the instability of wind power data and extract hidden temporal characteristics. Specifically, ensemble empirical mode decomposition decomposes the data once to obtain a set of intrinsic mode functions (IMFs), and then variational mode decomposition is used to decompose the fuzzy irregular IMF1 function twice. Finally, a bidirectional long short-term memory network (BiLSTM) based on particle swarm optimization (PSO) is applied to predict wind power data. The LSGAN-QMD-PSO-BiLSTM model proposed in this research is verified on a wind farm located in Spain, which indicates that the proposed model achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) errors of 100.6577 and 66.5175 kW, along with the highest R2 of 0.9639.

A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction

The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers.

Predictive maintenance of wind turbines based on digital twin technology

Predictive maintenance of wind turbines plays a crucial part in directing power grid dispatching and maintaining power grid security. In this paper, a way of ultra-short term wind power prediction relied on digital twin technology is proposed, which realizes actual time and accurate wind power prediction by building a digital model. Firstly, BP neural network (back propagation neutral network) was used to predict the wind power, and the initial predicted value of wind power was obtained. Then the meteorological data was substituted into the historical meteorological database to find similar meteorological conditions data, and the BP neural network predicted value was weighted to get the final digital twin predicted value. The simulation results indicate that this method can effectively enhance prediction accuracy of wind power.

Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM

Accurate prediction of short-term wind power plays an important role in the safe operation and economic dispatch of the power grid. In response to the current single algorithm that cannot further improve the prediction accuracy, this study proposes a combined wind power prediction model based on data processing, signal decomposition, and deep learning. First, outliers in the original data can affect prediction accuracy. This study detects the outliers by the Z-score method and fills them with cubic spline interpolation to ensure the integrity of the data. Second, for the volatility of wind power, the wind power time series is decomposed using the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN). The component complexity of the decomposition is calculated using the sample entropy (SE), and the components are reconstructed according to the SE size to improve the prediction accuracy. Finally, the traditional convolutional neural network (CNN) structure is improved and the bi-directional long short-term memory (BiLSTM) network is used to further extract the feature links between wind power data and superimpose the prediction results of each reconstructed component to obtain the final wind power prediction value. The experimental results demonstrate that the hybrid model proposed in this study has better performance in terms of prediction performance.

Recent advances in data-driven prediction for wind power

Wind power is one of the most representative renewable energy and has attracted wide attention in recent years. With the increasing installed capacity of global wind power, its nature of randomness and uncertainty has posed a serious risk to the safe and stable operation of the power system. Therefore, accurate wind power prediction plays an increasingly important role in controlling the impact of the fluctuations of wind power to in system dispatch planning. Recently, with the rapid accumulation of data resource and the continuous improvement of computing power, data-driven artificial intelligence technology has been popularly applied in many industries. AI-based models in the field of wind power prediction have become a cutting-edge research subject. This paper comprehensively reviews the AI-based models for wind power prediction at various temporal and spatial scales, covering from wind turbine level to regional level. To obtain in-depth insights on performance of various prediction methods, we review and analyze performance evaluation metrics of both deterministic models and probabilistic models for wind power prediction. In addition, challenges arising in data quality control, feature engineering, and model generalization for the data-driven wind power prediction methods are discussed. Future research directions to improving the accuracy of data-driven wind power prediction are also addressed.

Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network

Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of wind power prediction, in this study, a hybrid prediction model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, the wind power data were decomposed into a set of subsequences by using TVFEMD. Next, PSR was used to construct the corresponding phase space matrix for sequences, which were then divided into training sets, validation sets, and testing sets. Then, ESN was used for subsequence prediction. Finally, the predicted values of all the subseries were used to determine the final predicted power. To enhance the model performance, the sparrow search algorithm was improved in terms of the discoverer position update strategy, the follower position update strategy, and the population structure. IASSA was employed to synchronously optimize multiple parameters of PSR-ESN. The results revealed that the proposed model has higher applicability and prediction accuracy than existing models.

Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention

Accurate and stable prediction of regional wind power is crucial for optimal scheduling and renewable energy utilization in the power grid. In this paper, a novel multi-objective optimized recurrent neural network with temporal pattern attention (TPA) is proposed to address the randomness and uncertainty of wind farms in regional wind power prediction. Firstly, Taguchi method is applied to select the weather variables from wind farms, reducing redundancy and improving efficiency. Then, the stacked model is constructed using a denoising autoencoder (DAE) and gated recurrent unit (GRU), to improve the robustness and temporal correlation of the hidden states. The TPA is introduced to assign different weights to the hidden states, considering the multivariate relationships at different time steps. Furthermore, the Multi-objective slime mould algorithm (MOSMA) and variable weight multi-objective loss function (VMLF) are developed to optimize DGRU-TPA under multiple objectives to realize accurate and stable prediction. Finally, the experiment results demonstrate that nRMSE, nMAPE, and nSD of the proposed model are reduced by 26.36%, 24.05%, and 21.04% respectively, and qualification rate (QR) is increased by 13.56% compared to other models. The proposed model has achieved superior performance in regional prediction, which is crucial for effective grid management with increasing wind energy.

A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction

ABSTRACT Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

A hybrid hour-ahead wind power prediction model based on variational mode decomposition and bio-inspired LSTM

Wind power, as an eco-friendly renewable energy, has been widely integrated into modern power systems. The prediction accuracy of wind power is crucial to the secure operation of power systems. To improve the prediction accuracy, a hybrid hour-ahead wind power prediction method is presented in this paper. First, the variational mode decomposition (VMD) is used to decompose the original wind power sequences into a set of intrinsic mode functions (IMFs) with different frequencies. Then, the prediction model is formulated by using the long short-term memory (LSTM) network for each IMF. To enhance the LSTM, a bio-inspired algorithm named Harris Hawks optimization (HHO) is blended to optimize the parameters of each LSTM prediction model. The final prediction output power is thus obtained by integrating all prediction results from these individual IMFs, so that the novel VMD–HHO–LSTM prediction strategy is developed. Finally, case studies based on the real historical dataset are performed, demonstrating that the proposed hybrid hour-ahead wind power prediction model named VMD–HHO–LSTM has better prediction performance and higher applicability to multiple dataset scenarios.

Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar.

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer.

Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.

Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model

The volatility and intermittency of wind energy result in highly unpredictable wind power output, which poses challenges to the stability of the intact power system when integrating large-scale wind power. The accuracy of wind power prediction is critical for maximizing the utilization of wind energy, improving the quality of power supply, and maintaining the stable operation of the power grid. To address this challenge, this paper proposes a novel hybrid forecasting model, referred to as Hybrid WT–PSO–NARMAX, which combines wavelet transform, randomness operator-based particle swarm optimization (ROPSO), and non-linear autoregressive moving average with external inputs (NARMAX). The model is specifically designed for power generation forecasting in wind energy systems, and it incorporates the interactions between the wind system’s supervisory control and data acquisition’s (SCADA) actual power record and numerical weather prediction (NWP) meteorological data for one year. In the proposed model, wavelet transform is utilized to significantly improve the quality of the chaotic meteorological and SCADA data. The NARMAX techniques are used to map the non-linear relationship between the NWP meteorological variables and SCADA wind power. ROPSO is then employed to optimize the parameters of NARMAX to achieve higher forecasting accuracy. The performance of the proposed model is compared with other forecasting strategies, and it outperforms in terms of forecasting accuracy improvement. Additionally, the proposed Prediction Error-Based Power Forecasting (PEBF) approach is introduced, which retrains the model to update the results whenever the difference between forecasted and actual wind powers exceeds a certain limit. The efficiency of the developed scheme is evaluated through a real case study involving a 180 MW grid-connected wind energy system located in Shenyang, China. The proposed model’s forecasting accuracy is evaluated using various assessment metrics, including mean absolute error (MAE) and root mean square error (RMSE), with the average values of MAE and RMSE being 0.27% and 0.30%, respectively. The simulation and numerical results demonstrated that the proposed model accurately predicts wind output power.

A CNN encoder decoder LSTM model for sustainable wind power predictive analytics

Wind Power (WP) proliferates as one of the significant sustainable energies available in the form of temporal intervals. WP exists as a natural energy generating resource that depends on climatic parameters like wind speed, wind direction. These parameters are highly undetermined, thus collected over a fixed time interval. Hence, the nature of WP is quite uncertain over the time sequences and intricate to forecast. Accurate WP prediction has high significance for wind plants. It sequences the planning for different types of wind farm layouts, number and type of wind turbines required, peak load estimation, distribution areas and plans, cost and benefit risk mitigation etc. Long Short-Term Memory (LSTM) involves prediction using temporal relationships of data points collected over a period of time. The type of predictive analytics required in WP forecasting is very similar to the working of LSTM model. A hybrid Deep Learning (DL) based model using encoder decoder framework with Convolution Neural network (CNN) encoder with LSTM as decoder, named CNN Encoder-Decoder LSTM (CNN-ED-LSTM) is proposed in this study. Hence, to enhance the existing WP predictive analytics (WPPA), this paper focuses on optimization of WPPA using LSTM as the decoder and one-dimensional CNN as encoder. Evaluation of the proposed model is done using five different performance evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and computational time. Also, other traditional DL models such as VanillaLSTM, StackedLSTM, CNN-LSTM and Bidirectional-LSTM (Bi-LSTM) are assessed over the same metrics. Additionally, five statistical models namely, ARIMA-LSTM, AutoARIMA, AutoRegressive Integrated Mean Average (ARIMA), AutoRegressive Mean Average (ARMA) and AutoRegressive (AR), are evaluated. Then, the efficacy of CNN-ED-LSTM is compared with these conventional models of time series predictive analytics as well as traditional DL models that are already used in different studies for WPPA. The proposed CNN-ED-LSTM model for WPPA is performing better than traditional DL models from 3.2% to 9.6% in terms of MSE metric and 3.2–7.5% for RMSE metric.

Short-Term Wind Power Prediction Method Based on Combination of Meteorological Features and CatBoost

As one of the hot topics in the field of new energy, short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy. Therefore, a short-term wind power prediction method based on the combination of meteorological features and CatBoost is presented. Firstly, morgan-stone algebras and sure independence screening(MS-SIS) method is designed to filter the meteorological features, and the influence of the meteorological features on the wind power is explored. Then, a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element. Finally, a prediction method based on CatBoost network is constructed to further realize short-term wind power prediction. The National Renewable Energy Laboratory (NREL) dataset is used for experimental analysis. The results show that the short-term wind power prediction method based on the combination of meteorological features and CatBoost not only improve the prediction accuracy of short-term wind power, but also have higher calculation efficiency.

Wind Power Interval Prediction via an Integrated Variational Empirical Decomposition Deep Learning Model

As global demand for renewable energy increases, wind energy has become an important source of clean energy. However, due to the instability and unpredictability of wind energy, predicting wind power becomes one of the keys to resolving the instability of wind power. The current point prediction model of wind power output has limitations and randomness in processing information. In order to improve the prediction accuracy and efficiency of wind power, a multi-step interval prediction method (VMD-TCN) is proposed in this article, which uses variational modal decomposition and an improved temporal convolutional network model to predict wind power. Additionally, it introduces attention mechanism, further improving the prediction performance of the model. The method first uses empirical mode decomposition to decompose the wind power generation sequence into six parts and obtains the trend, oscillation and noise components of the output power sequence; then, it optimizes the parameters of the six components, respectively, and uses the interval prediction method combined with the temporal convolutional network to construct a new power prediction model. Experiments show that the proposed method can effectively improve the prediction performance of the power prediction model, and it has strong robustness in interval prediction and high sensitivity to load changes, which can well help power system scheduling and new energy consumption.

VMD-CAT: A hybrid model for short-term wind power prediction

Accurate wind power prediction is essential to optimize the wind power scheduling and maximize the profits. However, the inertia and time-varying property of the wind speed pose a challenge to the wind power prediction task. The existing prediction models fail to efficiently mitigate the negative influence of these properties on the prediction results. Therefore, their generalization abilities require a further improvement. In this paper, the historical wind power segment is decomposed into sub-signals, which are considered as the fluctuation patterns of the wind power series, the variable support then is employed to describe the inertia and time-varying properties for the fluctuation patterns. The component-attention mechanism is introduced to formulate the correlation-relationship between each fluctuation pattern and the historical wind power segment, this mechanism is used to replace the self-attention mechanism for the Transformer model. A hybrid model combined VMD and Transformer is utilized for predicting the future wind power. Experiments performed on an actual wind power series validate the efficiency of the proposed model.

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features

Accurate and reliable short-term forecasting of wind power is vital for balancing energy and integrating wind power into a grid. A novel hybrid deep learning model is designed in this study to increase the prediction accuracy of short-term wind power forecasting on a wind farm in Jiang County, Shanxi, China. The proposed hybrid deep learning model comprises variable mode decomposition (VMD), convolutional neural network (CNN), and gated recurrent unit (GRU). VMD substantially reduces the volatility of wind speed sequences. CNN automatically extracts complex spatial features from wind power data, and GRU can directly extract temporal features from historical input data. The forecasting accuracy of the combined VMD-CNN-GRU model is higher than that of any single model for wind power. The study used data obtained in 15 min intervals from the wind farm to determine the effectiveness of the proposed model against other advanced models. Compared with the other deep learning models, VMD-CNN-GRU is the best at short-term forecasting, with an RMSE of 1.5651, MAE of 0.8161, MAPE of 11.62%, and R2 of 0.9964. This method is valuable for practical applications and can be used to maintain safe wind farm operations in the future.

Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction

To improve the accuracy of short-term wind power prediction, a short-term wind power prediction model based on the LSTM model and multiple error correction is proposed. First, an affine wind power correction model based on assimilative migration is established to reduce the errors caused by false positives from the initial data. Then, a self-moving window LSTM prediction model based on the improved particle swarm optimization algorithm was established. By improving the particle swarm optimization algorithm, the optimal hidden neuron number and the optimal learning rate of the LSTM model were calculated to enhance the model’s accuracy. Definitively, the idea of error feedback prediction is used to correct the initial prediction error, and the prediction error is fed back to the LSTM model to reduce the error caused by the calculation of the LSTM model. By starting from the initial data error, model accuracy error, and model prediction error, multiple error correction of wind power is realized to improve the model accuracy. The simulation results show that the method improves the model’s prediction accuracy by using assimilative transfer and error feedback, contributing to the economic operation and sustainable development of the power system. Unlike traditional improvement ideas, the proposed improvement ideas do not involve the inherent characteristics of the original prediction methods. This method does not need to introduce other auxiliary methods and has good universality.

Recurrent neural network wind power prediction based on variational modal decomposition improvement

In order to avoid the problem that the traditional recurrent neural network (RNN) wind power prediction model cannot take into account both the law of wind power variation and the impact of sudden change factors, this paper proposes an improved cyclic neural network wind power prediction model based on variational modal decomposition (VMD). The VMD algorithm is used to decompose the output power of wind power into different frequency components and analyze the impact of different frequency components on the prediction model. Combined with the feature extraction ability of the neural network, it can reduce the impact of abrupt abnormal data on the prediction results and improve the real-time prediction accuracy of wind power. According to the historical data of an actual wind farm, the results show that the accuracy of the wind power prediction model based on VMD and the recurrent neural network is more than 85%, which is superior to the traditional RNN and the standard long short term memory wind power prediction model.