Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting. (March 2023)

• Record Type: Journal Article
• Authors: Ali, Mumtaz; Prasad, Ramendra; Xiang, Yong; Jamei, Mehdi; Yaseen, Zaher Mundher
• Is Part Of: Renewable energy. Volume 205(2023)
• Page Start: 731
• Page End: 746
• Publication Date: 2023-03
• Subjects: Robust local mean decomposition -- Ensemble modelling -- Random forest -- Coastal waves -- Significant wave height -- Energy management; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2023.01.108
• Languages: English
• ISSNs: 0960-1481

Abstract

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 t 0 and t 1 . 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.