A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting

Zhang, Wenyu; Zhi, Qu; Zhang, Kequan; Mao, Wenqian; Ma, Yining; Xu, Fan

doi:10.1016/j.enconman.2017.01.022

Cited by 322 publications

(109 citation statements)

References 25 publications

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“…Combining CEEMDAN, a flower pollination algorithm with chaotic local search, five ANN models and no negative constraint theory, Zhang, Qu et al, (2017) proposed a decomposition-ensemble approach to short-term wind speed forecasting. Also, Liu, Mi, and Li (2018) proposed a new framework using wavelet packet decomposition, CEEMDAN and the ANN model for multi-step forecasting of wind speed.…”

Section: Ceemdan-based Decomposition-ensemble Approachesmentioning

confidence: 99%

A decomposition-ensemble approach for tourism forecasting

Xie

Qian

Wang

2020

Annals of Tourism Research

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A B S T R A C TWith the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.

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Section: Ceemdan-based Decomposition-ensemble Approachesmentioning

confidence: 99%

A decomposition-ensemble approach for tourism forecasting

Xie

Qian

Wang

2020

Annals of Tourism Research

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“…Over the past few decades, numerous wind power forecasting approaches have been presented, which have enhanced the prediction accuracy of wind power series. However, since the relatively noisy and unstable characteristics of wind power data, wind power prediction by directly using original data would lead to substantial forecasting errors and poor performance [30]. Hence, the signal decomposition technique has been considered and applied for wind power forecasting to improve prediction performance, especially EMD [31] and EEMD [32].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Zhou

Huo

et al. 2019

Sustainability

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The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

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“…Therefore, based on previous studies of EEMD, Colominas proposed the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), which adds adaptive white noise smoothing pulse interference in decomposition, and utilizes the characteristic of mean Gaussian white noises whose mean equals to zero to make the decomposition of signal data more complete, thus to effectively eliminate mode mixing [35][36][37].…”

Section: Ceemdanmentioning

confidence: 99%

“…Therefore, based on previous studies of EEMD, Colominas proposed the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). The CEEMDAN method adds adaptive white noise smooth pulse interference in each decomposition to make the decomposition of the signal data more complete [35][36][37]. Jun and Qing [38] developed an effective combined model based on complete ensemble empirical mode decomposition with adaptive noise, permutation entropy and echo state network with leaky integrator neurons for medium-term power load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM ( The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model. Keywords: daily peak load forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); modified grey wolf optimization (MGWO); support vector machine (SVM)

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A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting

Cited by 322 publications

References 25 publications

A decomposition-ensemble approach for tourism forecasting

A decomposition-ensemble approach for tourism forecasting

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

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