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.

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.

Wind Power Forecast Based on Artificial Bee Colony Algorithm

2015 ◽  
Vol 738-739 ◽  
pp. 417-422 ◽  
Author(s):  
Xin Zhang ◽  
Guo Chu Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Optimal Parameters ◽  
Power Prediction ◽  
Bee Colony ◽  
Forecast Precision ◽  
Wind Power Forecast ◽  
Wind Power Prediction ◽  
Svm Model

In view of the traditional support vector machine (SVM) model in wind power prediction parameter selection problems, this paper introduced a model which using artificial colony algorithm to seek the optimal parameters of support vector machine. The experimental results show that the SVM model of artificial swarm optimization application and prediction is effective, makes the forecast precision is improved.

The Wind Power Forecast Model Based on Improved EMD and SVM

2014 ◽  
Vol 694 ◽  
pp. 150-154 ◽  
Author(s):  
Guo Chu Chen ◽  
Xin Zhang ◽  
Zhi Wei Guan
Keyword(s):  
Wind Power ◽  
Predictive Accuracy ◽  
Spline Interpolation ◽  
Forecast Model ◽  
Support Vector ◽  
Power Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Wind Power Forecast ◽  
Wind Power Prediction

In order to improve the predictive accuracy of short-term wind power, a prediction model based on improved empirical mode decomposition (EMD) and support vector machine (SVM) is constructed. As to the problems of basic EMD, it is proposed to use the steady point meaning sifting method instead of spline envelope meaning sifting method, to improve the overshoots/undershoots caused by traditional cubic spline interpolation. Wind power series can be decomposed into different series by improved EMD, and then SVM is used to forecast power by each component. The total wind power prediction result is obtained through reconstructing at last. Case study shows that the predictive accuracy has significantly been improved by comparing with other models.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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
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