Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

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.

Accurate and timely wind power forecast is difficult because of the volatility and intermittency of wind energy, which are strongly influenced by meteorological conditions. In order to solve this problem, this research suggests a data-driven wind power prediction technique based on Elman neural networks and an enhanced generative adversarial network (GAN). First, outliers in the supervisory control and data acquisition (SCADA) power and wind speed data are found by combining the methods of density clustering and regression threshold truncation. To guarantee the temporal continuity of wind power data with the original characteristics, minority class samples are then generated using an enhanced GAN and added to the dataset. In order to obtain an accurate wind power prediction, the balanced data is then fed into a prediction model that combines the ABC algorithm and Elman neural networks. The fitness value is the mean squared error (MSE) of the training data, and the optimization objective is the connection weights of the Elman neural network. Results from experiments show that the suggested model outperforms earlier state-of-the-art techniques and significantly increases wind power prediction accuracy. It also has the advantages of high stability and quick convergence speed, and it can capture the long-term dependencies of wind power data.

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.

With the rapid development of wind energy, the imperative for precise wind power predictions has intensified, with the crux lying in forecasting wind speeds. The accurate short-term (1 to 3 days) forecast of wind speeds at the hub height in boundary layer poses a significant scientific challenge. Generating such forecasts for wind farms 1 to 3 days in lead time necessitates reliance on global weather forecast products and the WRF model. In pursuit of heightened accuracy, artificial intelligence (AI) algorithms are employed to refine WRF-predicted wind speeds based on observational data. This study draws upon observational data from five operational wind farms over three years, employing diverse deep time-series models, to examine the effectiveness and limitations of these models in post-processing corrections for WRF-predicted wind speeds. Based on our examination, we conclude that: 1) Transformer-based models have significant untapped potential, with the Pyraformer model emerging as a well-suited temporal model for post-processing corrections in wind speed and power predictions. 2) Traditional full-attention mechanisms are less effective, highlighting the importance of sparse attention as a vital approach for capturing temporal correlations in such problems. 3) The optimal model demonstrates a reduction of approximately 20% in RMSE for single-point post-processing corrections. In addition, wind speed prediction accuracy reaches around 86%, and power prediction accuracy is approximately 82%. 4) AI-based post-processing corrections may encounter challenges, including the underestimation for high-value and difficulties in reproducing forecasts below the average value.

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 forecasting (WPF) is essential in order to operate power systems with increased wind power penetration in an efficient and secure way. WPF techniques using statistical modeling require the availability of historical data. This paper presents a WPF tool that is self-constructed and self-adaptive. It can, therefore, be implemented from the beginning of a wind farm operation, with very limited or no historical information and it can adapt automatically to any future wind farm enhancements or retrofits. The algorithms converge after a few days of operation, as shown by the application of the method in a real wind farm case-study. The results are compared with a state-of-art wind power prediction model.

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.

Wind power is one of the most large-scale new energy sources. But wind power instability will affect power grid safety, which is in a great need for wind power forecasting algorithms. To accurately predict wind power and reduce power grid fluctuations, it proposes a new wind power forecasting (WPF) algorithm based on long short-term memory (LSTM) neural network using wind farm real operation data. First, the wind farm power data are de-averaged and divided into two different sets in order to meet the requirements of the algorithm. Then, structure of the LSTM neural network is designed and hyper-parameters are adjusted to improve accuracy of forecasting. Finally, the definite LSTM neural network is used for forecasting the power data in time series to derive the power forecasting value, which is reduced and evaluated according to the original size. The results show that compared with other forecasting methods, in short-term WPF at different time scales, this algorithm has smaller errors in power forecasting results, which is also suitable for long-term WPF.

Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning

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.

Wind power forecasting has complex nonlinear features and behavioral patterns across time scales, which is a severe test for traditional forecasting techniques. To address the multi-scale problem in wind power forecasting, this paper innovatively proposes an ultra-short-term forecasting model LFformer based on Legendre-Fourier, which firstly focuses on the important information in the input sequences by using the encoder-decoder architecture, and then scales the range of the original data with the Devlin normalization method, and then utilizes the Legendre polynomials to The data sequence is projected into a bounded dimensional space, the historical data is compressed using feature representation, then feature selection is performed using the low-rank approximation method of Fourier Transform, the prediction is inputted into the multilayer perceptron through the multi-scale mixing mechanism, and finally the results are outputted after back-normalization. The experimental results show that compared with the existing prediction methods, the model realizes the improvement of prediction accuracy and stability, especially in the ultra-short-term prediction scenario, with obvious advantages. The research results are not only valuable for improving the overall operational efficiency of the wind power system, but also help to enhance the stable operation of the power grid, which provides strong technical support and guarantee for wind power enterprises to improve the competitiveness of bidding for Internet access in the power market competition.

Incorporating wind energy on a large scale into power systems presents challenges for the operation and control of the grid. To enhance the safety of power grid operation, accurate short-term forecasting of wind power is necessary, as it minimizes the impact of randomness. Considering the uncertainty and prediction issues associated with wind power, this paper introduces a CNN–GRU ultra-short-term wind power prediction model. This model relies on multichannel signals, including data such as wind speed, wind direction, climate conditions, and historical power outputs collected from wind farms. These data types contribute to the formation of a comprehensive multichannel signal for wind power. Following that, the CNN method extracts both global and partial features from these signals. Concurrently, features are extracted from past power outputs based on their time series. These features are then combined with the ones obtained from the convolution process. Subsequently, these combined features are input into a fully connected network. This step is crucial for blending multichannel information and generating forecast results. To validate the model, it was tested using data from a wind farm located in a specific region of Sichuan Province. According to our experimental results, the model demonstrates a high level of accuracy in computation and robust generalization ability.

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.

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.

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.