scholarly journals Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Energies ◽  
2013 ◽  
Vol 6 (4) ◽  
pp. 1887-1901 ◽  
Author(s):  
Guo-Feng Fan ◽  
Shan Qing ◽  
Hua Wang ◽  
Wei-Chiang Hong ◽  
Hong-Juan Li
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Support Vector Regression Model ◽  
Model Based ◽  
Mode Decomposition ◽  
Auto Regression

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

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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