Short-Term Wind Speed Forecasting by Using Chaotic Theory and SVM

2013 ◽  
Vol 300-301 ◽  
pp. 842-847 ◽  
Author(s):  
Cai Hong Zhu ◽  
Ling Ling Li ◽  
Jun Hao Li ◽  
Jian Sen Gao
Keyword(s):  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Phase Space Reconstruction ◽  
Percentage Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Wind Speed Forecast

The wind speed forecast is the basis of the wind power forecast. The wind speed has the characteristics of random non-smooth so obviously that its precise forecast is extremely difficult. Therefore, a forecasting method based on the theory of chaotic phase-space reconstruction and SVM was put forward in this paper and a forecasting model of Chaotic Support Vector Machine was built. In order to improve the precision and generalization ability, the key parameters in the phase space reconstruction and the key parameters of SVM were carried out joint optimization by using particle swarm algorithm in the paper. Then the optimal parameters were brought into the forecasting model to forecast short-term wind speed. The above method was applied to wind speed forecast of a wind farm in Inner Mongolia, China. In the experiments of computer simulation, the absolute percentage error of forecasting results was only 12.51%, which showed this method was effective for short-term wind speed forecast.

The Forecasting of Short-Term Wind Speed on Wind Farm Based on Phase Space Reconstruction and Neural Network

2012 ◽  
Vol 246-247 ◽  
pp. 496-500
Author(s):  
Ying Ying Su ◽  
Fei Ma ◽  
Hai Yan Zhang ◽  
Zhi Qiang Liao ◽  
Peng Jun
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Absolute Error ◽  
Phase Space Reconstruction ◽  
The Novel ◽  
Short Term ◽  
Sample Data ◽  
The Mean

The forecasting precision of short-term wind speed is not high for its chaos and time-varying. Aimed at the problem, the novel data space is reconstructed with the best embedding dimension and time delay according to the phase space reconstruction. On the basis, neural network (NN) is used as the modeling tool with the novel sample data. Meanwhile, the structure of NN is confirmed compared with the others on the precision. In the end, the model of short-term wind speed is able to be obtained. The results show that the method is available and the Mean absolute error (MAE) is decreased to 16.2% for 2 hours.

Short-Term Wind Speed and Power Prediction Using Fuzzy Information Granulation-Support Vector Machine

2012 ◽  
Vol 608-609 ◽  
pp. 814-817
Author(s):  
Xiao Fu ◽  
Dong Xiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Farm ◽  
Support Vector ◽  
Fuzzy Information ◽  
Short Term ◽  
Power Fluctuation ◽  
Data Set ◽  
Information Granulation ◽  
Fuzzy Information Granulation

The power fluctuation of wind turbine often causes serious problems in electricity grids. Therefore, short term prediction of wind speed and power as to eliminate the uncertainty determined crucially the development of wind energy. Compared with physical methods, support vector machine (SVM) as an intelligent artificial method is more general and shows better nonlinear modeling capacity. A model which combined fuzzy information granulation with SVM method was developed and implemented in short term future trend prediction of wind speed and power. The data, including the daily wind speed and power, from a wind farm in northern China were used to evaluate the proposed method. The prediction results show that the proposed model performs better and more stable than the standard SVM model when apply them into the same data set.

scholarly journals Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

scholarly journals Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jianzhou Wang ◽  
Qingping Zhou ◽  
Haiyan Jiang ◽  
Ru Hou
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Wind Farm ◽  
Hypothesis Test ◽  
Recurrence Plot ◽  
Support Vector ◽  
Short Term ◽  
Cuckoo Optimization Algorithm ◽  
Input Variables

This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

2013 ◽  
Vol 300-301 ◽  
pp. 189-194 ◽  
Author(s):  
Yu Sun ◽  
Ling Ling Li ◽  
Xiao Song Huang ◽  
Chao Ying Duan
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Neural Network Prediction ◽  
Random Fluctuations ◽  
The Impact

To avoid the impact which is caused by the characteristics of the random fluctuations of the wind speed to grid-connected wind power generation system, accurately prediction of short-term wind speed is needed. This paper designed a combination prediction model which used the theories of wavelet transformation and support vector machine (SVM). This improved the model’s prediction accuracy through the method of achiving change character in wind speed sequences in different scales by wavelet transform and optimizing the parameters of support vector machines through the improved particle swarm algorithm. The model showed great generalization ability and high prediction accuracy through the experiment. The lowest root-mean-square error of 200 samples was up to 0.0932 and the model’s effect was much stronger than the BP neural network prediction model. It provided an effective method for predicting wind speed.

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