scholarly journals Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

Energies ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Yan Chen ◽  
Wei-Chiang Hong ◽  
Wen Shen ◽  
Ning Huang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Squares ◽  
Search Algorithm ◽  
Harmony Search ◽  
Load Forecasting ◽  
Harmony Search Algorithm ◽  
Fuzzy Time Series ◽  
Support Vector ◽  
Electric Load

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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