Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification

Mao, Xuemin; Yang, Jie

doi:10.1109/cimca.2006.218

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Support Vector Regression (SVR) is an important application branch of SVM. It is an intelligent algorithm based on statistical learning theory and the principle of structural risk minimization, which has obvious advantages for small sample and nonlinear regression prediction [17][18]. The difference between SVM and SVR is that the final sample points of SVR only belong to one type and the hyperplane it finds is to minimize the total deviation of the distances from all the sample points to the hyperplane.…”

Section: Svrmentioning

confidence: 99%

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Sun¹,

Yang²,

Wang³

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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Traditional power load or electricity forecasting methods have used only limited meteorological information. Specifically, these methods either only select one of maximal, minimal and average temperature as the influencing indicator, or only capture the very basic meteorological information, instead of exploring the nonlinear relationship between temperature and loads in depth. To overcome the shortcomings of the traditional methods, this paper proposes a new medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators. This method constructs the encoder and decoder between weather features and loads. Moreover, this paper combines this method with XGBoost and SVR, which can improve the accuracy of medium- and long-term forecasting algorithm in the power grid field effectively. The simulation results of this synthesized model show that average daily prediction accuracy rate of the year is 95.37% and the average monthly prediction accuracy rate of the year is 98.02%.

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Section: Svrmentioning

confidence: 99%

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Sun¹,

Yang²,

Wang³

et al. 2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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show abstract

“…In recent years, the Support Vector Machine(SVM) has become a hotspot of the intellectual technology field because of its superior learning ability [1]- [3] . Many scholars lead ε -insensitive loss function into SVM, and advance ε -Support Vector regression Machine algorithm [4] , which is used widely in the field of system optimization, optimal control, forecasting and so on.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of ϵ-Support Vector Machine by Genetic Algorithm

Zhang

Wang³

2009

2009 Fifth International Conference on Natural Computation

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The ε -Support Vector regression Machine is a promising artificial intelligence technique, in which the regression algorithm has already been used in solving the nonlinear function approach successfully. Most users select parameters for an SVM by rule of thumb, so they frequently fail to generate the optimal parameters effect for the function. This has restricted effective use of SVM to a great degree. In this paper, the authors use genetic algorithm to solve the SVM parameters optimization problem. Simulation result shows that the method has high precision and possesses certain practical application significance.

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Adaptive transfer of vehicle density in the distributed traffic simulation

Potužák

2008

2008 Conference on Human System Interactions

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Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification

Cited by 8 publications

References 2 publications

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Parameter Optimization of ϵ-Support Vector Machine by Genetic Algorithm

Adaptive transfer of vehicle density in the distributed traffic simulation

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Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification

Cited by 8 publications

References 2 publications

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

A novel medium- and long-term load forecasting method based on sensitivity analysis of multi meteorological indicators

Parameter Optimization of &#x003F5;-Support Vector Machine by Genetic Algorithm

Adaptive transfer of vehicle density in the distributed traffic simulation

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Parameter Optimization of ϵ-Support Vector Machine by Genetic Algorithm