An advanced multistage multi-step tidal current speed and direction prediction model

Nima Safari, Benyamin Khorramdel, Alireza Zare, Chi Yung Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Non-stationarity and non-linearity of the tidal current speed (TCS) and tidal current direction (TCD) time series are among the main barriers for enhancing the TCS and TCD prediction accuracy. In this regard, this paper proposes an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) which is employed to decompose the non-stationary TCS and TCD time series into several components (modes) with unique characteristics. Then, to capture the nonlinear pattern of TCS and TCD in different modes, several prediction engines based on least squares support vector machine (LSSVM) are developed. To modify the prediction error which occurs in predicting different components, a prediction modification stage based on a combination of extreme learning machines (ELMs) is utilized to reconstruct the final prediction values. The proposed TCS and TCD prediction model, named ICEEMDAN-LSSVM-ELM, has been evaluated using the data recorded from Shark river entrance, NJ. Performance of the proposed prediction model is compared with various well-developed benchmark models.

Original languageEnglish
Title of host publication2017 IEEE Electrical Power and Energy Conference, EPEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538608173
DOIs
Publication statusPublished - 2 Jul 2017
Externally publishedYes
Event2017 IEEE Electrical Power and Energy Conference, EPEC 2017 - Saskatoon, Canada
Duration: 22 Oct 201725 Oct 2017

Publication series

Name2017 IEEE Electrical Power and Energy Conference, EPEC 2017
Volume2017-October

Conference

Conference2017 IEEE Electrical Power and Energy Conference, EPEC 2017
Country/TerritoryCanada
CitySaskatoon
Period22/10/1725/10/17

Keywords

  • Complete ensemble empirical mode decomposition adaptive noise
  • extreme learning machine
  • prediction
  • tidal current direction
  • tidal current speed

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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