scholarly journals Prediction on sunspot activity based on fuzzy information granulation and support vector machine

2018 ◽  
Author(s):  
Lingling Peng ◽  
Haisheng Yan ◽  
Zhigang Yang
Keyword(s):  
Support Vector Machine ◽  
Sunspot Activity ◽  
Support Vector ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

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 Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine

2020 ◽  
pp. 307-307
Author(s):  
Tao Wang ◽  
Tingyu Ma ◽  
Dongsong Yan ◽  
Jing Song ◽  
Jianshuo Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
District Heating ◽  
Series Data ◽  
Support Vector ◽  
Fuzzy Information ◽  
District Heating System ◽  
Information Granulation ◽  
Heating Load ◽  
Fuzzy Information Granulation

District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load.

A Fig-Based Method for Prediction Alumina Concentration

2012 ◽  
Vol 4 (4) ◽  
pp. 41-50 ◽  
Author(s):  
Jun Yi ◽  
Jun Peng ◽  
Taifu Li
Keyword(s):  
Time Series Data ◽  
Prediction Method ◽  
Information Support ◽  
Series Data ◽  
Support Vector ◽  
Redundant Information ◽  
Alumina Concentration ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

Existing prediction model can not be established accurately as a result of there is often a lot of redundant information in observed values of alumina concentration. A prediction method based on fuzzy information granulation for alumina concentration is proposed to solve above problem. In the proposed approach, theory of fuzzy information granulation was used to granulate time-series data of alumina cell. Granulated data can not only reflect the characteristics of original but also reduce redundant information. Support vector machine was employed as predictor. The experimental results using real data of 170KA operating aluminum cell from a factory demonstrate the efficiency of the designed method and the viability of the technique.

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