A novel multivariable hybrid model to improve short and long-term significant wave height prediction

Pang, Junheng; Dong, Sheng

doi:10.1016/j.apenergy.2023.121813

Cited by 8 publications

(1 citation statement)

References 56 publications

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“…They also looked into the influencing factors, such as the input-output ratio and the combination of input features. Pang and Dong [27] incorporated recursive quantitative analysis (RQA) and the improved complete integrated empirical modal decomposition of adaptive noise (ICEEMDAN) into a deterministic and stochastic component decomposition (DSD) method. The results showed that wind data had a favorable effect on long-term forecasts and that the DSD methodology improved forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Ouyang,

Gao,

Zhang

et al. 2024

JMSE

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Significant Wave Height (SWH) is a crucial parameter in ocean wave dynamics, impacting coastal safety, maritime transportation, and meteorological research. Building upon the TimesNet neural network, a recent advancement in the realm of time series prediction in deep learning, this study proposes an integrated approach combining Empirical Mode Decomposition (EMD) with TimesNet, introducing the EMD-TimesNet model for SWH forecasting. The TimesNet model’s multidimensional spatial mapping guarantees effective historical information extraction, while the EMD approach makes it easier to decompose subsequence characteristics inside the original SWH data. The predicted Root Mean Square Error (RMSE) and Correlation Coefficient (CC) values of the EMD-TimesNet model are 0.0494 m and 0.9936; 0.0982 m and 0.9747; and 0.1573 m and 0.9352 at 1 h, 3 h, and 6 h, respectively. The results indicate that the EMD-TimesNet model outperforms existing models, including the TimesNet, Autoformer, Transformer, and CNN-BiLSTM-Attention models, both in terms of overall evaluation metrics and prediction performance for diverse sea states. This integrated model represents a promising advancement in enhancing the accuracy of SWH predictions.

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Section: Introductionmentioning

confidence: 99%

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Ouyang,

Gao,

Zhang

et al. 2024

JMSE

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Configuration and Operation Optimization for Industrial Steam Power System Sustainable Retrofit Considering Renewable Energy Integration

Li,

Yang,

Tian

et al. 2024

Advanced Sustainable Systems

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Renewable energy integration and operational optimization are crucial in energy sustainability and decarbonization, especially for industrial steam power systems (SPS). This study establishes an SPS superstructure that integrates wind, solar, and biomass energy. A mixed‐integer nonlinear programming (MINLP) model is developed to determine an optimal retrofit strategy that minimizes the life cycle cost, which includes carbon emission cost and energy cost. To solve this high‐dimensional complex optimization problem, a multistage strategy fusion differential evolution algorithm with dynamic partitioning of the SVM feasible domain (SVM‐MS‐DE) is developed. The results from the optimal strategy demonstrate a 9.02% reduction in the system's total cost and a 9.64% decrease in carbon emission through the incorporation of wind and solar energy. Additionally, the sensitivity analysis on biomass ratio, carbon emission price, energy demand, and carbon emission limits reveal that the region can contribute significantly to renewable energy initiatives. Several recommended policies are provided to encourage enterprises to move towards sustainable development.

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Development of pyramid neural networks for prediction of significant wave height for renewable energy farms

Mahdavi-Meymand,

Sulisz

2024

Applied Energy

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A novel multivariable hybrid model to improve short and long-term significant wave height prediction

Cited by 8 publications

References 56 publications

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Configuration and Operation Optimization for Industrial Steam Power System Sustainable Retrofit Considering Renewable Energy Integration

Development of pyramid neural networks for prediction of significant wave height for renewable energy farms

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