Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Zhang, Yagang; Chen, Yinchuan

doi:10.1007/s11356-021-16997-3

Cited by 22 publications

(6 citation statements)

References 42 publications

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“…CEEMDAN extends the EMD method and effectively addresses the issues of modal aliasing and noise residuals through the adaptive incorporation of the noise factor in the EMD decomposition procedure [14]. The CEEMDAN algorithm introduces an adaptive noise mechanism that gradually extracts noise components from the original signal through multiple iterations and utilizes them as an estimate of the noise.…”

Section: Basic Algorithm 21 Ceemdan Algorithmmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Accurate prediction of PV power is essential to maximize solar energy utilization, enhance the performance of PV power generation systems, and ensure the dependability of power infrastructures. In this paper, an approach based on CS-LSTM neural network is proposed for short term power forecasting. The proposed approach involves processing the data using CEEMDAN, followed by the application of the CS algorithm to determine the optimal LSTM hyperparameters. To evaluate the accuracy and dependability of the model, CEEMDAN-CS-LSTM is compared to other models. Experiments show that CEEMDAN-CS-LSTM outperforms the other models in terms of prediction accuracy and stability.

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Section: Basic Algorithm 21 Ceemdan Algorithmmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Moreover, the LSTM model containing the attention mechanism not only performs better in time series prediction but also maintains long-time memory while significantly lowering the training time. The validation was performed successively by Chen and Zhang [ 31 ].…”

Section: Theoretical Basismentioning

confidence: 99%

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Zhu,

Qin,

Meng

et al. 2024

PLoS ONE

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The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation.

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“…Zhang and Chen [37] introduced a hybrid model on the basis of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Singular Value Decomposition (SVD), and Particle Swarm Optimization (PSO) to predict the speed of winds. CEEMDAN was integrated with SVD to decompose and de-noise the data and the PSO was used in the process of optimization.…”

Section: Applications Of Wind Energy Based On Machine Learning and De...mentioning

confidence: 99%

A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

Bhansali,

Narasimhulu,

Pérez de Prado

et al. 2023

Energies

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Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods based on Computational Intelligence (CI) are considered an effective way to generate renewable instruments. The energy-related complexities of developing such methods are dependent on the vastness of the data sets and number of parameters needed to be covered, both of which need to be carefully examined. The most recent and significant researchers in the field of learning-based approaches for renewable challenges are addressed in this article. There are several different Deep Learning (DL) and Machine Learning (ML) approaches that are utilized in solar, wind, hydro, and tidal energy sources. A new taxonomy is formed in the process of evaluating the effectiveness of the strategies that are described in the literature. This survey evaluates the advantages and the drawbacks of the existing methodologies and helps to find an effective approach to overcome the issues in the existing methods. In this study, various methods based on energy conversion systems in renewable source of energies like solar, wind, hydro power, and tidal energies are evaluated using ML and DL approaches.

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Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Cited by 22 publications

References 42 publications

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

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