Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach

Jamei, Mehdi; Ali, Mumtaz; Karbasi, Masoud; Xiang, Yong; Ahmadianfar, Iman; Yaseen, Zaher Mundher

doi:10.1016/j.apenergy.2022.119925

Cited by 39 publications

(8 citation statements)

References 71 publications

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“…Herein, some preprocessing approaches can be used to decompose wave time series data. The use of decomposition methods such as empirical mode decomposition (EMD) [29], a multi-stage multivariate variational mode decomposition (VMD) [30], complete ensemble empirical mode decomposition (CEEMDAN) [31], and discrete wavelet transformation [32] has been shown to be effective in various forecasting applications. To reduce non-linearity and nonstationarity in wave data, Hao et al [33] proposed a hybrid method based on EMD-LSTM.…”

Section: Introductionmentioning

confidence: 99%

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

Dokur,

Erdogan,

Salari

et al. 2024

IET Renewable Power Gen

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While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.

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Section: Introductionmentioning

confidence: 99%

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

Dokur,

Erdogan,

Salari

et al. 2024

IET Renewable Power Gen

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“…Huang et al 26 applied the EMD–GM(1,1) model to predict landslide deformation and compared it with the traditional GM(1,1) model, and found that EMD–GM(1,1) had higher prediction accuracy. Jamei et al 27 used the Multivariate Variational Mode Decomposition (MVMD) decomposition method with the LSSVM model applied to predict the daily wave energy in coastal areas, this research can help authorities in the field of renewable and sustainable energy for better planning and development. Jamei et al 28 used a novel decomposition method, namely time varying filter-based empirical mode decomposition (TVF–EMD), before predicting daily flood levels at two sites in the Clarence River, Australia.…”

Section: Introductionmentioning

confidence: 99%

Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model

Guo

Wen

Zhang

et al. 2023

Sci Rep

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Accurate medium and long-term runoff forecasts play a vital role in guiding the rational exploitation of water resources and improving the overall efficiency of water resources use. Machine learning is becoming a common trend in time series forecasting research. Least squares support vector machine (LSSVM) and grey model (GM(1,1)) have received much attention in predicting rainfall and runoff in the last two years. “Decomposition-forecasting” has become one of the most important methods for forecasting time series data. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition method has powerful advantages in dealing with nonlinear data. Least squares support vector machine (LSSVM) has strong nonlinear fitting ability and good robustness. Gray model (GM(1,1)) can solve the problems of little historical data and low serial integrity and reliability. Based on their respective advantages, a combined CEEMDAN–LSSVM–GM(1,1) model was developed and applied to the runoff prediction of the lower Yellow River. To verify the reliability of the model, the prediction results were compared with the single LSSVM model, the CEEMDAN–LSSVM model and the CEEMDAN–support vector machines (SVM)–GM(1,1). The results show that the combined CEEMDAN–LSSVM–GM(1,1) model has a high accuracy and the prediction results are better than other models, which provides an effective prediction method for regional medium and long-term runoff prediction and has good application prospects.

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“…Variational mode decomposition (VMD) (Dragomiretskiy and Zosso, 2014) has overcome the disadvantages of EMD and is currently the most effective decomposition technique (Duan et al, 2022). Models combining VMD and neural networks are applied in forecasting various time series data, for example, stock price prediction (Bisoi et al, 2019), air quality index prediction (Wu and Lin, 2019), wind power prediction (Duan et al, 2022), runoff prediction (Zuo et al, 2020), and wave energy prediction (Neshat et al, 2022;Jamei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm

Ji,

Han,

Jiang

et al. 2023

Ocean Sci.

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Abstract. The present work proposes a prediction model of significant wave height (SWH) and average wave period (APD) based on variational mode decomposition (VMD), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks. The wave sequence features were obtained using VMD technology based on the wave data from the National Data Buoy Center. Then the SWH and APD prediction models were established using TCNs, LSTM, and Bayesian hyperparameter optimization. The VMD–TCN–LSTM model was compared with the VMD–LSTM (without TCN cells) and LSTM (without VMD and TCN cells) models. The VMD–TCN–LSTM model has significant superiority and shows robustness and generality in different buoy prediction experiments. In the 3 h wave forecasts, VMD primarily improved the model performance, while the TCN had less of an influence. In the 12, 24, and 48 h wave forecasts, both VMD and TCNs improved the model performance. The contribution of the TCN to the improvement of the prediction result determination coefficient gradually increased as the forecasting length increased. In the 48 h SWH forecasts, the VMD and TCN improved the determination coefficient by 132.5 % and 36.8 %, respectively. In the 48 h APD forecasts, the VMD and TCN improved the determination coefficient by 119.7 % and 40.9 %, respectively.

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Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach

Cited by 39 publications

References 71 publications

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM(1,1) model

Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm

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