Parameter C Optimizing for Robust ε-Support Vector Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2045-2048
Author(s):  
Yuan Lv ◽  
Zhong Gan
Keyword(s):  
Experimental Data ◽  
Support Vector Regression ◽  
System Parameter ◽  
Forecast Accuracy ◽  
Regression Method ◽  
Support Vector ◽  
Penalty Parameter ◽  
Optimal Method ◽  
Good Forecast ◽  
Selection Of

In case of experimental data contaminated with errors and noise, the robust ε-support vector regression has good forecast accuracy and high generalization ability. However, it depends on the selection of system parameter. Firstly, this paper introduces the robust ε-support vector regression method. Secondly, as the experiments prove, the new method achieves high forecast accuracy by virtue of the optimal penalty parameter C. Finally, the optimal method of parameter C is presented in the last section.

Robustness Analysis of Novel ε-Support Vector Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2049-2052
Author(s):  
Yuan Lv ◽  
Zhong Gan
Keyword(s):  
Normal Distribution ◽  
Support Vector Regression ◽  
Robustness Analysis ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Kolmogorov Test ◽  
Hyper Plane ◽  
Good Forecast

The key to the robust ε-support vector regression algorithm is searching for the optimal regression hyper plane while data with disturbance in the X-direction. In the paper, the optimal regression hyper plane and the optimal separating hyper plane are compared and analyzed. By means of Kolmogorov test, it is can be deduced that the testing errors of the robust ε-support vector regression experiments follow normal distribution. The result demonstrates that the algorithm has good forecast accuracy and high robustness.

scholarly journals Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1320
Author(s):  
Yuanyuan Sun ◽  
Gongde Xu ◽  
Na Li ◽  
Kejun Li ◽  
Yongliang Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Heat Dissipation ◽  
Hot Spot ◽  
Load Capacity ◽  
Regression Method ◽  
Support Vector ◽  
Temperature Prediction ◽  
Support Vector Regression Model ◽  
Load Rate

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

scholarly journals Support Vector Regression Method for Regional Economic Mid- and Long-Term Predictions Based on Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingyu Dong
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Support Vector Regression ◽  
Wireless Network ◽  
Regression Method ◽  
Network Communication ◽  
Support Vector ◽  
Network Coverage ◽  
Data Packets

In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

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