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

Dai, Shuyu; Niu, Dongxiao; Yan, Li

doi:10.3390/en11010163

Cited by 64 publications

(41 citation statements)

References 32 publications

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“…Using CEEMDAN and a support vector machine optimized by a modified grey wolf optimization algorithm (MGWO-SVM), Dai, Niu, and Li (2018) proposed a novel decomposition-ensemble approach for the time series forecasting of daily peak loads. The investigation suggests that using CEEMDAN can help achieve noise reduction for the non-stationary time series of daily peak loads and make the time series more regular.…”

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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“…The flowchart of CEEMDAN is shown in Figure 2. The CEEMDAN process can be defined as follows [43,44]:…”

Section: Ceemdanmentioning

confidence: 99%

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

et al. 2019

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During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

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“…Later, the authors put forward an improved version of CEEMDAN to obtain decomposed components with less noise and more physical meaning [33]. The CEEMDAN has succeeded in wind speed forecasting [34], electricity load forecasting [35], and fault diagnosis [36][37][38]. Therefore, CEEMDAN may have the potential to forecast crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

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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

Cited by 64 publications

References 32 publications

A decomposition-ensemble approach for tourism forecasting

A decomposition-ensemble approach for tourism forecasting

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

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