Research on Hybrid ARIMA and Support Vector Machine Model in Short Term Load Forecasting

2006 ◽  
Author(s):  
Yujun He ◽  
Youchan Zhu ◽  
Dongxing Duan
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Machine Model ◽  
Short Term Load Forecasting

Power Short-Term Load Forecasting Based on QPSO-SVM

2012 ◽  
Vol 591-593 ◽  
pp. 1311-1314
Author(s):  
Xing Tong Zhu ◽  
Bo Xu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Quantum Particle ◽  
Close Contact ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Short Term Load Forecasting

The values of parameters of support vector machine have close contact with its forecast accuracy. In order to accurately forecast power short-term load,we presented a power short-term load forecasting method based on quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally, the support vector machine was used for power short-term load forecasting. In the proposed method such factors impacting loads as meteorology,weather and date types are comprehensively considered. The experimental results show that the root-mean-square relative error of the proposed method is only 1.90%, which is less than those of SVM and PSO-SVM model by 2.29% and 2.80%, respectively.

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