Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

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.

The Application of Entropy Method in Wind Power Combined Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 3043-3046
Author(s):  
Kang Ping Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Entropy Method ◽  
Support Vector ◽  
Power Prediction ◽  
Combination Forecasting ◽  
Wind Power Forecast ◽  
Wind Power Prediction ◽  
Short Term Forecasting ◽  
Information Fusion Technique

A new combined method of wind power prediction based on entropy method is proposed according to information fusion technique. Firstly, Carry out the wind-power forecast with BP neural network, radial basis function neural network and support vector machine respectively. Then, weights of combination forecasting can be obtained according to the degree of variation of prediction error sequence. Case study was carried out to investigate the validity of the novel algorithm and the results illustrated that the proposed combined model can improve the short term forecasting accuracy of wind power effectively by tracking the change of wind power.

scholarly journals Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mao Yang ◽  
Lei Liu ◽  
Yang Cui ◽  
Xin Su
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Power Grid ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Wide Range

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

Short-term wind power prediction based on improved small-world neural network

2018 ◽  
Vol 31 (7) ◽  
pp. 3173-3185 ◽  
Author(s):  
Shuang-Xin Wang ◽  
Meng Li ◽  
Long Zhao ◽  
Chen Jin
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Small World ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

scholarly journals Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 512 ◽  
Author(s):  
Chao Fu ◽  
Guo-Quan Li ◽  
Kuo-Ping Lin ◽  
Hui-Juan Zhang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Wind Power ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction ◽  
Svm Model

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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