scholarly journals Uncertain Machine Load Forecasting Based on Least Squares Support Vector Machine

2021 ◽  
Author(s):  
Qiaofeng Meng
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Strong Correlation ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Load Forecasting ◽  
Support Vector ◽  
Shop Scheduling ◽  
Machine Load ◽  
Forecasting Method

Machine state is a very important constraint for job shop scheduling. For the uncertainty machine state, the paper proposes a machine load forecasting method based on support vector machine. The method reduces complexity and improves efficiency by eliminating a large number of unrelated input factors and selecting a small number of input parameters with strong correlation. The efficiency of the algorithm is verified by the production workshop instance.

Spatial Electric Load Forecasting Based on Least Squares Support Vector

2014 ◽  
Vol 986-987 ◽  
pp. 542-545 ◽  
Author(s):  
Yan Bin Li ◽  
Yun Li ◽  
Le Cao ◽  
Wei Guo Li
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Artificial Neural Network Model ◽  
Linear Equations ◽  
Load Forecasting ◽  
Regression Coefficients ◽  
Support Vector ◽  
Electric Load ◽  
Forecasting Method ◽  
Quadratic Programming Problems

This paper proposes a new spatial load forecasting method for distribution network based on least squares support vector machine. The method adopt data, the characteristic of which is similar with forecast sample, to training in order to obtain the regression coefficients and bias, which we need to do the forecasting.Atthe same time,compare with artificial neural network model,The least squares support vector machine transforms quadratic programming problems into linear equations, thus avoiding the insensitive loss function, greatly reducing the computational complexity and further improving the accuracy of the prediction model. Finally, the effectiveness and practicality are verified by examples.

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