Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm

2019 ◽  
Vol 98 (2) ◽  
pp. 1107-1136 ◽  
Author(s):  
Zichen Zhang ◽  
Wei-Chiang Hong
Keyword(s):  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Load Forecasting ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Electric Load ◽  
Dragonfly Algorithm ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Electric Load Forecasting

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 Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

2020 ◽  
Vol 10 (16) ◽  
pp. 5542 ◽  
Author(s):  
Rui Li ◽  
Chao Ran ◽  
Bin Zhang ◽  
Leng Han ◽  
Song Feng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Operational Conditions ◽  
Mode Decomposition ◽  
Adaptive Noise

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

scholarly journals A Novel Hybrid Method for Short-Term Power Load Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Huang Yuansheng ◽  
Huang Shenhai ◽  
Song Jiayin
Keyword(s):  
Empirical Mode Decomposition ◽  
Hybrid Method ◽  
Load Forecasting ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Residual Term ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Power Load ◽  
Power Load Forecasting

Influenced by many uncertain and random factors, nonstationary, nonlinearity, and time-variety appear in power load series, which is difficult to forecast accurately. Aiming at locating these issues of power load forecasting, an innovative hybrid method is proposed to forecast power load in this paper. Firstly, ensemble empirical mode decomposition (EEMD) is used to decompose the power load series into a series of independent intrinsic mode functions (IMFs) and a residual term. Secondly, genetic algorithm (GA) is then applied to determine the best weights of each IMF and the residual term named ensemble empirical mode decomposition based on weight (WEEMD). Thirdly, least square support vector machine (LSSVM) and nonparametric generalized autoregressive conditional heteroscedasticity (NPGARCH) are employed to forecast the subseries, respectively, based on the characteristics of power load series. Finally, the forecasted power load of each component is summed as the final forecasted result of power load. Compared with other methods, the forecasting results of this proposed model applied to the electricity market of Pennsylvania-New Jersey-Maryland (PJM) indicate that the proposed model outperforms other models.

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