scholarly journals A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiping Wang ◽  
Yaqi Wang
Keyword(s):  
Load Forecasting ◽  
Vital Role ◽  
Support Vector ◽  
Electric Load ◽  
Short Term ◽  
Power Utility ◽  
Mode Decomposition ◽  
Short Term Load Forecasting ◽  
Optimization Parameters ◽  
Gain Optimization

Short-term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD-PSO-SVR forecasting model for short-term load forecasting based on empirical mode decomposition (EMD), support vector regression (SVR), and particle swarm optimization (PSO), also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD-SVR model, traditional SVR model, and PSO-SVR model, the result indicates that the proposed EMD-PSO-SVR model performs more effectively and more stably in forecasting the residential short-term load.

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.

scholarly journals Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4890
Author(s):  
Mengran Zhou ◽  
Tianyu Hu ◽  
Kai Bian ◽  
Wenhao Lai ◽  
Feng Hu ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Variational Mode Decomposition ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Mode Decomposition ◽  
The Impact ◽  
Electric Load Forecasting

Short-term electric load forecasting plays a significant role in the safe and stable operation of the LO system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

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.

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