Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method

Karijadi, Irene; Chou, Shuo-Yan; Dewabharata, Anindhita

doi:10.1016/j.renene.2023.119357

Cited by 19 publications

(2 citation statements)

References 49 publications

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“…However, the output of wind power generation has strong intermittency and instability [7] . After the integration of large-scale wind farms, the injection of highly fluctuating wind power will cause local voltage fluctuations, which will affect the quality of electricity and the safe and stable operation of the power grid, bringing certain difficulties and challenges to the optimization and scheduling of the power grid [8] . Therefore the instability of wind power generation has become an important obstacle to the effective utilization and management of wind energy [9,10] .…”

Section: Introduction1mentioning

confidence: 99%

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

Li,

Miao

2024

Chin. J. Electr. Eng.

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For short-term wind power forecasting, an interval A2-C1 type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic systems(IT2 A2-C1 FLS) method based on extended Kalman filter (EKF) optimization algorithm is proposed, where "A" means antecedent and "C" consequent. Compared with the type-1 FLS model, IT2 TSK FLS method can simultaneously model both intra-individual and inter-individual uncertainty, and further optimize the antecedent and consequent parameters using EKF to further improve the forecasting performance. The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region, under the same conditions, it is also compared with the type-1 TSK FLS and IT2 TSK FLS methods with the back propagation (BP) and particle swarm optimization (PSO) algorithms, as well as IT2 A2-C0 TSK FLS methods with EKF. Experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to other FLS methods in performance,showing its effectiveness and application potential.

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Section: Introduction1mentioning

confidence: 99%

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

Li,

Miao

2024

Chin. J. Electr. Eng.

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“…However, empirical mode decomposition has the disadvantage of mode aliasing. After improvements based on the EMD algorithm, integrated empirical mode decomposition (EEMD) [3] and fully adaptive noise ensemble empirical mode decomposition (CEEMDAN) can be obtained [4] , several algorithms of fully adaptive noise ensemble empirical mode decomposition ICEEMDAN [5] . The wavelet threshold denoising algorithm can achieve the purpose of denoising by distinguishing the amplitude-frequency characteristics of signals and noise [6] .…”

Section: Introductionmentioning

confidence: 99%

Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

yin,

shi

2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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The settlement deformation of subway tunnels during the construction and operation stages is a relatively complex nonlinear process. The original settlement deformation monitoring data will be affected by some noise during the collection process. In order to reduce the impact of noise and errors in the subsequent prediction process, a joint denoising model based on the improved empirical mode decomposition (ICEEMDAN) algorithm, wavelet threshold denoising and NLMS adaptive filtering was constructed. The model first uses ICEEMDAN to decompose the original data. After decomposing the intrinsic modal component IMF, it divides it into high-frequency and low-frequency components. Then the wavelet threshold is used to remove the components with high correlation coefficients in the high-frequency and low-frequency components. Noise and NLMS adaptive filtering are processed, and finally the additive reconstruction is performed to obtain the denoised data. Experimental analysis results show that compared with ICEEMDAN, wavelet threshold denoising and NLMS adaptive filtering algorithms, the joint denoising model has better denoising effect, higher correlation coefficient, signal-to-noise ratio and smaller mean square root error.

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CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

Garai,

Paul,

Yeasin

et al. 2024

Neural Process Lett

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Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.

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Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method

Cited by 19 publications

References 49 publications

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

Short-term wind power forecasting using interval A2-C1 type-2 TSK FLS method with extended Kalman filter algorithm

Deformation data analysis of shield tunnel based on ICEEMDAN- wavelet -NLMS joint model

CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

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