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Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

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  • Ali, Mumtaz
  • Prasad, Ramendra

Abstract

Data-intelligent algorithms designed for forecasting significant height of coastal waves over the relatively short time period in coastal zones can generate crucial information about enhancing the renewable energy production. In this study, a machine learning model is designed and evaluated for forecasting significant wave height (Hs) within the eastern coastal zones of Australia. The extreme learning machine (ELM) model is coupled with an improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN) to design the proposed ICEEMDAN-ELM model. This model incorporates the historical lagged series of Hs as the model's predictor to forecast future Hs. The ICEEMDAN algorithm demarcates the original Hs data from January-2000 to March-2018, recorded at 30-min intervals, into decomposed signals i.e., intrinsic mode functions (IMFs) and a residual component. After decomposition, the partial autocorrelation function is determined for each IMF and the residual sub-series to determine the statistically significant lagged input dataset. The ELM model is applied for forecasting of each IMF by incorporating the significant antecedent Hs sub-series as inputs. Finally, all the forecasted IMFs are summed up to obtain the final forecasted Hs. The results are benchmarked with those from an online sequential extreme learning machine (OSELM) and random forest (RF) integrated with ICEEMDAN, i.e., the ICEEMDAN-OSELM and ICEEMDAN-RF models. The proposed ICEEMDAN-ELM model is tested geographically at two coastal sites of the Queensland state, Australia. The testing performance of all the standalone (ELM, OSELM, RF) and integrated models (ICEEMDAN-ELM, ICEEMDAN-OSELM, ICEEMDAN-RF), according to robust statistical error metrics, is satisfactory; however, the hybrid ICEEMDAN-ELM model is found to be a beneficial Hs forecasting tool in accordance to high performance accuracy. The proposed ICEEMDAN-ELM model can be considered as a pertinent decision-support framework and is vital for designing of reliable ocean energy converters.

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  • 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.
    • Handle: RePEc:eee:rensus:v:104:y:2019:i:c:p:281-295
    DOI: 10.1016/j.rser.2019.01.014
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