EFAMamba: An Efficient Feature Aggregation Model based on Mamba for Accurate Wind Power Forecasting

Accurate short-term forecasting of wind farm power generation is of great significance to economic development and stable operation of power system. In order to improve the accuracy of wind power prediction, this paper presents a short-term wind power prediction method based on empirical mode decomposition and deep learning. We firstly decompose historical data into two-dimensional tensors by using empirical mode decomposition, and then extract the local features of data by using convolutional neural network. The features are transmitted to the bidirectional long short-term memory for prediction. The measured wind power data form wind farm in Xinjiang are used for verification. We use root mean square error and mean absolute error as evaluation indexes to test the algorithm. Results of the experiment show that the reconstructed two-dimensional tensor is beneficial to the ability of convolutional neural network to extract local features, and the prediction accuracy of our method is higher than that of other traditional prediction algorithms.

With the gradual warming of the global climate, wind energy, as one of the representatives of clean energy, has been rapidly developed in countries worldwide. However, wind power generation has strong intermittency and variability, constantly impacting the stability of the power grid. In order to improve the accuracy of wind power prediction, this paper proposes a BP neural network model optimized by genetic algorithm (GA) for wind power prediction. This model uses GA to optimize the initial weights and thresholds of the BP neural network to improve the computational accuracy and convergence speed of the model, effectively avoiding the problem of a single BP neural network model easily falling into local minima and the slow training speed of GA algorithm. By preprocessing and normalizing meteorological data such as weather, temperature, and wind direction, a wind power prediction model is constructed, and the prediction results show that the model effectively improves the computational accuracy and convergence speed compared to the traditional BP neural network model. This result proves the feasibility of using GA to optimize BP neural network for wind power prediction in this paper.

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.

This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction.

With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R2 of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges.

With the rapid increase in wind power, its strong randomness has brought great challenges to power system operation. Accurate and timely ultra-short-term wind power prediction is essential for the stable operation of power systems. In this paper, an LsAdam–LSTM model is proposed for ultra-short-term wind power prediction, which is obtained by accelerating the long short-term memory (LSTM) network using an improved Adam optimizer with loss shrinkage (LsAdam). For a specific network topology, training progress heavily depends on the learning rate. To make the training loss of LSTM shrink faster with standard Adam, we use the past training loss-changing information to finely tune the next learning rate. Therefore, we design a gain coefficient according to the loss change to adjust the global learning rate in every epoch. In this way, the loss change in the training process can be incorporated into the learning progress and a closed-loop adaptive learning rate tuning mechanism can be constructed. Drastic changes in network parameters will deteriorate learning progress and even make the model non-converging, so the gain coefficient is designed based on the arctangent function with self-limiting properties. Because the learning rate is iteratively tuned with past loss-changing information, the trained model will have better performance. The test results on a wind turbine show that the LsAdam–LSTM model can obtain higher prediction accuracy with much fewer training epochs compared with Adam–LSTM, and the prediction accuracy has significant improvements compared with BP and SVR models.

For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.

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.

Due to the stochastic and intermittent nature of wind power, the accuracy of wind power prediction(WPP) is of great significance to the grid with increasing wind farm penetration. Currently the methods of WPP is various and uneven. Hence forecasting methods are detailedly classified and summarized in this paper, and the basic ideas, advantages and disadvantages of these methods are discussed in detail. The paper presents the analysis of state-of-the-art worldwide research, and the outlook of the main research directions of WPP.

Accurate wind power prediction can reduce the negative impact of wind power on power grid and reduce the operation cost of power system. Wind power forecasting is of great significance in spot trading, but the instability of wind power series brings difficulties to forecasting. With the rapid development of deep learning technology, prediction algorithm based on deep learning has been widely used in wind power prediction. The long short-term memory (LSTM) network performs well in a variety of prediction scenarios, but there is still room for improvement. In order to improve the prediction accuracy of LSTM in wind power prediction, this paper adopts a new gating mechanism in LSTM network, which can improve the context information modeling ability of LSTM network. The experimental results based on real data show that the proposed improved LSTM prediction model can improve the prediction accuracy of LSTM prediction model in ultra-short term wind power prediction scenarios.

A novel approach to ultra-short-term multi-step wind power predictions based on encoder–decoder architecture in natural language processing

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

The accuracy of wind power forecasting has a very important influence on the safe and stable operation of power system. However, wind power prediction is very difficult, especially under the environment of massive data. This paper presents a novel multi-step ahead wind power prediction model based on recurrent neural network (RNN) with long short-term memory (LSTM) unit or gated recurrent unit (GRU) to improve the accuracy. Firstly, an overall forecasting framework for wind power with diverse forms of optional hybrid models is proposed. Moreover, an innovative LSTM/GRU-based forecasting model is developed with a wind speed correction process using numerical weather prediction (NWP) data. Through the application of GRU network, the correction process can provide corrected wind speed at the predicted moment as an input of the forecasting model. Finally, the experimental results demonstrate the superiority of the proposed RNN approaches as compared to the ARIMA method and SVM method while the main distinctions between the LSTM and GRU network are also illustrated.

Very-short-term wind power prediction is of great significance for the safe and stable operation of power system. As a well-known time series model, vector autoregressive (VAR) model is very suitable for very-short-term forecasting, and it can consider the temporal and spatial correlation among multiple wind farms to improve the forecasting accuracy. However, when there are a large number of wind farms, VAR model is prone to over-fitting. Slow calculation speed is also the problem of VAR model. This paper adds regularization terms in estimating coefficients to obtain a sparse coefficient matrix, which helps to avoid over-fitting. Online VAR model is used to improve the calculation speed, and a specific improved online VAR method is proposed for forecasting wind power output from new-built wind farms. This method is tested in a specific case, and the prediction accuracy and calculation speed are better than the traditional VAR model. In addition, the improved online VAR method can accurately predict wind power output of new-built wind farms with little historical data.

Wind power predication is significant for the safety and stability of power system operation. Due to the power forecasting single model or combination model without considering the information fusion and the influence of forecasting error on power dispatching, forecasting results are not accurate enough. In the paper, a novel combination model for wind power forecasting with error evaluation parameter based on cross entropy theory is proposed. Wind power prediction is seen as information fusion, and the weights of different wind power prediction methods is set according to their cross levels. Besides, an error evaluation parameter is also proposed to estimate the different influences of positive and negative error on power scheduling. Application results in an actual wind farm show that the cross levels of different wind power prediction methods can be identified effectively and prediction precision is improved by the proposed method.