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Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting

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  • Ali, Mumtaz
  • Prasad, Ramendra
  • Xiang, Yong
  • Jamei, Mehdi
  • Yaseen, Zaher Mundher

Abstract

A robust short-term significant wave height (Hs) modelling framework based on an ensemble local mean decomposition method integrated with random forest (i.e., En-RLMD-RF) is developed. The robust local mean decomposition (RLMD) decomposed the Hs data series into three subseries; amplitude modulation, frequency modulation and the low-frequency product function (PFs). The partial autocorrelation function was employed to determine the correlation-based significant predictor signals between the PFs at t0 and t1. Then the statistically significant PFs were incorporated into the random forest (RF) to construct the RLMD-RF model. The RLMD-RF based forecasted PFs were used again in the RF model as input predictors resulting in an ensemble-based RLMD-RF (i.e., En-RLMD-RF) model for forecasting short-term Hs. The En-RLMD-RF model is validated and compared with RF, extreme learning machine (ELM) and multiple linear regression (MLR) models and their hybrids RLMD-RF, RLMD-ELM, RLMD-MLR, En-RLMD-ELM and En-RLMD-MLR counterparts using a set of performance metrics. The results demonstrated that the En-RLMD-RF model generates better forecasting accuracy against the benchmarking models. This study is beneficial for the application and optimization of more clean energy resources worldwide for sustained energy generation.

Suggested Citation

  • Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Jamei, Mehdi & Yaseen, Zaher Mundher, 2023. "Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting," Renewable Energy, Elsevier, vol. 205(C), pages 731-746.
    • Handle: RePEc:eee:renene:v:205:y:2023:i:c:p:731-746
    DOI: 10.1016/j.renene.2023.01.108
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    References listed on IDEAS

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    1. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Deo, Ravinesh C., 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    3. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Li, Yan & Adamowski, Jan F., 2018. "Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting," Applied Energy, Elsevier, vol. 217(C), pages 422-439.
    4. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    5. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    6. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    7. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    8. Cuadra, L. & Salcedo-Sanz, S. & Nieto-Borge, J.C. & Alexandre, E. & Rodríguez, G., 2016. "Computational intelligence in wave energy: Comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1223-1246.
    9. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    10. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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