scholarly journals Short Term Load Forecasting System Based on Support Vector Kernel Methods

2014 ◽  
Vol 6 (3) ◽  
pp. 93-102 ◽  
Author(s):  
Lal Hussain ◽  
Sajjad Nadeem M ◽  
Syed Ahsen Ali Shah
Keyword(s):  
Kernel Methods ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Forecasting System

The Application of Chaotic Particle Swarm Optimization Algorithm in Power System Load Forecasting

2012 ◽  
Vol 614-615 ◽  
pp. 866-869 ◽  
Author(s):  
Yu Hong Zhao ◽  
Xue Cheng Zhao ◽  
Wei Cheng
Keyword(s):  
Optimization Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Error ◽  
Parameters Selection ◽  
Short Term Load Forecasting ◽  
Search Speed ◽  
Chaotic Particle Swarm Optimization

The support vector machine (SVM) has been successfully applied in the short-term load forecasting area, but its learning and generalization ability depends on a proper setting of its parameters. In order to improve forecasting accuracy, aiming at the disadvantages like man-made blindness in the parameters selection of SVM, In this paper, the chaos theory was applied to the PSO (particles swarm optimization) algorithm in order to cope with the problems such as low search speed and local optimization. Finally, we used it to optimize the support vector machines of short-term load forecasting model. Through the analysis of the daily forecasting results, it is shown that the proposed method could reduce modeling error and forecasting error of SVM model effectively and has better performance than general methods.

scholarly journals Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1093 ◽  
Author(s):  
Wei-Chiang Hong ◽  
Guo-Feng Fan
Keyword(s):  
Support Vector Regression ◽  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Middle Term ◽  
Mode Decomposition ◽  
Random Term ◽  
Forecasting Performance ◽  
Short Term Load Forecasting

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

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