Application of the GRA and SVM for Forecast of China Grain Production

2012 ◽  
Vol 433-440 ◽  
pp. 1106-1111
Author(s):  
Jin Fa Shi ◽  
He Jun Jiao
Keyword(s):  
Support Vector Machine Model ◽  
Production Control ◽  
Food System ◽  
Support Vector ◽  
Production Data ◽  
Grain Production ◽  
Machine Model ◽  
Factors Influencing ◽  
Svm Model ◽  
On Line

This paper proposes a new method: GRA-SVM model which is composed of GRA and SVM to predict grain production through annual production data. In view of the fact that the complexity and incomplete information of grain production system, the primary factors influencing the grain production is decided on the basis of the grey ralational analysis of the grain producing system, then, the grey ralational analysis and support vector machine model is established by the principle of the support vection machine regression. The application case proved that the proposed method can improve the feasibility of the program in grain production, and it is suitable for on-line grain production control for food system.

The Research of Least Square Support Vector Machine Model and its Simplified Method

2012 ◽  
Vol 241-244 ◽  
pp. 1719-1723
Author(s):  
Wen Jie Zhao ◽  
Tao Zhang
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Least Square ◽  
Simplified Model ◽  
Support Vector ◽  
Machine Model ◽  
Training Samples ◽  
Svm Model ◽  
Simplified Method ◽  
Simulation Results

A simplified structure of the least square support vector machine (LS-SVM) model is proposed in this paper. Under the premise that the accuracy of LS-SVM model is unchanged, a small amount of training samples are chosen, which further fit this model by LS-SVM modeling. Finally, a typical nonlinear problem is taken as example to test the performance of this simplified model and the simulation results show that this simplified method proposed in this paper is effective.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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