Data mining and wind power prediction: A literature review

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

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.

Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases.

In the traditional weather classification prediction method based on clustering, the feature quantity is difficult to fully reflect the change information of complex weather, which will weaken the reliability of classification. Therefore, this paper proposes a wind power prediction model based on meteorological process division. Firstly, this method uses the correlation coefficient to analyze the correlation between various meteorological factors and wind power output in numerical weather and then uses the wind speed after wavelet denoising to divide the weather fluctuation process and extract the fluctuation characteristics of each fluctuation process. Secondly, the meteorological process is divided by a hierarchical clustering algorithm, and the types of meteorological processes are judged by a dynamic time planning algorithm. Finally, the wind power prediction model of a long short-term memory (LSTM) network is established based on different meteorological processes. The results show that this method can provide a reference for improving the accuracy and interpretability of short-term wind power forecasting.

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

To reduce the influence of the random fluctuation on wind power prediction, a new ultra-short-term wind power prediction model, based on wavelet decomposition (WD), variational mode decomposition (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method is based on the double decomposition and LSSVM, where the wind power sequence is decomposed by WD into low- and high-frequency components, which are further decomposed by VMD to obtain many modal components with tendency and periodicity. Multiple LSSVM prediction models are then established with historical wind power data and weather data as the inputs to obtain the predicted values of the multiple modal components. The final predicted values of wind power are achieved by data fusion of outputs of these LSSVM models. The experimental results show that the MAPE (mean absolute percentage error) of the combined prediction model is 4.66%, which is the best compared with nine benchmark models. This demonstrates the high performance of the proposed WD-VMD-LSSVM model for short-term prediction of wind power.

Electricity produced by the wind is a significant way to utilize renewable wind energy. Because of the volatility and uncertainty of wind power, existing forecasting systems need to significantly increase their prediction accuracy. Therefore, this paper suggests a Long short-term memory neural network-based wind power forecast model (LSTM). First, data are gathered to identify the model's input and output; next, an LSTM-based wind power prediction model is built; this creates a mapping between the model's input and the output of the predicted wind power; and finally, a case simulation analysis is applied to predict the wind power in accordance with the results. Compared to the GRU, ANN, and BP models, the LSTM-based wind power prediction model established in this paper's MAE evaluation index is reduced by 7.18%,31.74% and 17.83%. So the model established in this paper can improve the reliability and effectiveness of wind power prediction.

In the 21st century, energy demand has become a global problem, China's wind power energy jumps forward from start to large-scale development and its use also shows the characteristic of diversified development and thus requires the research of its related technology and personnel to ensure that the new energy wind power industry can further develop healthily.Based on this, this paper study from research on the development prediction and evaluation model of new energy wind power.The continued development of new energy wind power industry, on one hand, has a higher requirement of technology and needs some technically sophisticated technical personnel, on the other hand, the continuous improvement of technology level is bound to promote the continuous development of the biotechnology industry.So according to the current new wind energy development needs, study, evaluate and forecast new energy technologies talent demand and skill progress levels, which has extremely important practical significance for the development of new energy wind power.

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.

Spatially transferable machine learning wind power prediction models: [formula omitted]logit random forests

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

In recent years, the development of the wind power industry has been particularly rapid. The research of wind power forecasting technology is of great significance for ensuring the stability of the entire power system and improving the competitiveness of wind power in the power industry. In this paper, the wavelet denoising WD algorithm has good applicability in signal denoising, and a wind power prediction model based on WD-GA-SVM is established for a single wind turbine. Firstly, preprocess the related data to get the initial historical sequence, and then use the wavelet denoising method to reduce the noise of the historical data, and the revised historical data is closer to the actual data. Finally, the automatic optimization feature of the GA algorithm is used to complete the parameter optimization of the support vector machine, and the SVM model is optimized. The research results show that the method described in this paper effectively improves the accuracy of the real-time wind power prediction model.

Wind power generation is currently one of the most promising power generation technologies. It is particularly important to improve the prediction accuracy of wind power output, which can effectively reduce the impact on the grid when wind power is connected to the grid. Based on the fractal model, this paper integrates it with the wind power prediction model, and combines the custom K nearest neighbor algorithm to evaluate the prediction effect using multi-dimensional indicators. Finally, taking the data of a wind farm in northwest china as an example, compared it with the prediction model of random forest, support vector machine and gradient boosting decision tree prediction model to verify the effectiveness of the prediction algorithm in this paper.

With the large-scale wind integration into power grid, the intermittent and stochastic nature of wind power possess a threat to the safety of power grid, and wind power prediction has become one of the most important solutions to the current grid connection problem. However, the prediction error in point prediction of wind power cannot be ignored, so it is necessary to make probability prediction of wind power and improve reliable information to the power grid dispatch department. In this paper, by combing with the wavelet decomposition technology and the long short-term memory (LSTM) network, an ultra short-term probability model is proposed. Firstly, the original time series are smoothed by wavelet decomposition technology, then the LSTM network prediction model of each sub-series sample is developed. The Gaussian distribution function of the prediction error is obtained by using the maximum likelihood estimation method, and finally the probability interval prediction of the future wind power in four hours could be realized. Finally, by using the data collected from a wind farm in northeast china, a numerical example is presented to illustrate the usefulness of the proposed method, which show that combining wavelet decomposition with deep learning method can improve the accuracy of prediction, improve the interval reliability of probability prediction, and enhance the generalization ability of the model.