A Proposed Algorithms for Tidal in-Stream Speed Model

Hamed H. H. Aly; M. E. El-Hawary

doi:doi:10.11648/j.ajee.20130101.11

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A Proposed Algorithms for Tidal in-Stream Speed Model

Hamed H. H. Aly, M. E. El-Hawary

Published in American Journal of Energy Engineering (Volume 1, Issue 1)

Received: 8 February 2013 Accepted: Published: 10 March 2013

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Abstract

In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.

Published in	American Journal of Energy Engineering (Volume 1, Issue 1)
DOI	10.11648/j.ajee.20130101.11
Page(s)	1-10
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Power System Modeling, Tidal Currents, Forecasting, ANN, Fourier Series Based On Least Squares

References

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[23]	Hamed H. H. Aly, and M. E. El-Hawary, "A Proposed ANN and FLSM Hybrid Model for Tidal Current Magnitude and Direction Forecasting" accepted at the IEEE Journal of Ocean Engineering, 2013.
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[31]	Hamed H. Aly "Forecasting, Modeling, and Control of Tidal currents Electrical Energy Systems"PhD thesis, Halifax, Canada. 2012.

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APA Style

Hamed H. H. Aly, M. E. El-Hawary. (2013). A Proposed Algorithms for Tidal in-Stream Speed Model. American Journal of Energy Engineering, 1(1), 1-10. https://doi.org/10.11648/j.ajee.20130101.11

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ACS Style

Hamed H. H. Aly; M. E. El-Hawary. A Proposed Algorithms for Tidal in-Stream Speed Model. Am. J. Energy Eng. 2013, 1(1), 1-10. doi: 10.11648/j.ajee.20130101.11

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AMA Style

Hamed H. H. Aly, M. E. El-Hawary. A Proposed Algorithms for Tidal in-Stream Speed Model. Am J Energy Eng. 2013;1(1):1-10. doi: 10.11648/j.ajee.20130101.11

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@article{10.11648/j.ajee.20130101.11,
  author = {Hamed H. H. Aly and M. E. El-Hawary},
  title = {A Proposed Algorithms for Tidal in-Stream Speed Model},
  journal = {American Journal of Energy Engineering},
  volume = {1},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ajee.20130101.11},
  url = {https://doi.org/10.11648/j.ajee.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20130101.11},
  abstract = {In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.},
 year = {2013}
}

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TY  - JOUR
T1  - A Proposed Algorithms for Tidal in-Stream Speed Model
AU  - Hamed H. H. Aly
AU  - M. E. El-Hawary
Y1  - 2013/03/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ajee.20130101.11
DO  - 10.11648/j.ajee.20130101.11
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 1
EP  - 10
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20130101.11
AB  - In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.
VL  - 1
IS  - 1
ER  -

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Author Information

Hamed H. H. Aly

Department of Electrical and Computer Engineering, Dalhousie University, Halifax, Nova Scotia, Canada, B3H 4R2
M. E. El-Hawary

Department of Electrical and Computer Engineering, Dalhousie University, Halifax, Nova Scotia, Canada, B3H 4R2

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APA Style

Hamed H. H. Aly, M. E. El-Hawary. (2013). A Proposed Algorithms for Tidal in-Stream Speed Model. American Journal of Energy Engineering, 1(1), 1-10. https://doi.org/10.11648/j.ajee.20130101.11

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ACS Style

Hamed H. H. Aly; M. E. El-Hawary. A Proposed Algorithms for Tidal in-Stream Speed Model. Am. J. Energy Eng. 2013, 1(1), 1-10. doi: 10.11648/j.ajee.20130101.11

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AMA Style

Hamed H. H. Aly, M. E. El-Hawary. A Proposed Algorithms for Tidal in-Stream Speed Model. Am J Energy Eng. 2013;1(1):1-10. doi: 10.11648/j.ajee.20130101.11

Copy | Download

@article{10.11648/j.ajee.20130101.11,
  author = {Hamed H. H. Aly and M. E. El-Hawary},
  title = {A Proposed Algorithms for Tidal in-Stream Speed Model},
  journal = {American Journal of Energy Engineering},
  volume = {1},
  number = {1},
  pages = {1-10},
  doi = {10.11648/j.ajee.20130101.11},
  url = {https://doi.org/10.11648/j.ajee.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20130101.11},
  abstract = {In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - A Proposed Algorithms for Tidal in-Stream Speed Model
AU  - Hamed H. H. Aly
AU  - M. E. El-Hawary
Y1  - 2013/03/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ajee.20130101.11
DO  - 10.11648/j.ajee.20130101.11
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 1
EP  - 10
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20130101.11
AB  - In this paper we propose four models for tidal current speed and direction magnitude forecasting model. The first model is a Fourier series model based on the least squares method (FLSM), the second model is an artificial neural network (ANN), the third model is a hybrid of FLSM and ANN and the fourth model is a hybrid of ANN and FLSM for monthly forecasting of tidal current speed. These proposed models are ranked in order depending on their performance. These models are validated by using another set of data (tidal current direction). The proposed hybrid model of FLSM and ANN is highly accurate and outperforms. This study was done using data collected from the Bay of Fundy in 2008.
VL  - 1
IS  - 1
ER  -

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