TY - GEN
T1 - An advanced multistage multi-step tidal current speed and direction prediction model
AU - Safari, Nima
AU - Khorramdel, Benyamin
AU - Zare, Alireza
AU - Chung, Chi Yung
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - Complete ensemble empirical mode decomposition adaptive noise
KW - extreme learning machine
KW - prediction
KW - tidal current direction
KW - tidal current speed
UR - http://www.scopus.com/inward/record.url?scp=85050396620&partnerID=8YFLogxK
U2 - 10.1109/EPEC.2017.8286148
DO - 10.1109/EPEC.2017.8286148
M3 - Conference article published in proceeding or book
AN - SCOPUS:85050396620
T3 - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017
SP - 1
EP - 6
BT - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017
Y2 - 22 October 2017 through 25 October 2017
ER -