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

| Peer-Reviewed

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)

Published: 10 March 2013

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2013. Published by Science Publishing Group

Keywords

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

References

[1]	"Tidal Stream" Available online (November 2010), http://www.tidalstream.co.uk/html/background.html.
[2]	"Marine Current Turbines" Available online (October 2010), http://peswiki.com/index.php/Directory:Marine_Current_Turbines_Ltd#How_it_Works.
[3]	Darwin, G.H., on an apparatus for facilitating the reduction of tidal observations. Proceedings of the Royal Society, Series A 52, 345–376, 1892; Available at: http://archive.org/details/philtrans02858224.
[4]	Doodson, A. T., The Harmonic Development of the Tide-generating Potential, Proc. Roy. Soc., London, pp 305-329, 1923. Available at: http://archive.org/details/philtrans08044568.
[5]	Doodson, A.T., The analysis and predictions of tides in shallow water. International Hydrographic Review: Monaco 33, 85–126, 1958.
[6]	John, V. "Harmonic tidal current constituents of the Western Arabian Gulf from moored current measurements." Coastal Engineering, Vol. 17,pp. 145–151, 1992.
[7]	French, M. N. , Krajewski, W. F. & Cuykendall, R. R. "Rainfall forecasting in space and time using a neural network", Journal of Hydrological Sciences, 1992.
[8]	Raman, H. & Sunilkumar, N. "Multivariate modeling of water resources time series using artificial neural networks", Journal of Hydrological Sciences, pp. 145 - 163, 1995.
[9]	Christian Dawson & Robert Wilby "An artificial neural network approach to rainfall- runoff modelling", Journal of Hydrological Sciences, pp. 47- 66, 1988.
[10]	Coulibaly, P., Anctil, F., and Bobee, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach,Journal of Hydrological Sciences, pp. 244–257, 2000.
[11]	T.L. Lee, and D.S. Jeng "Application of Artificial Neural Networks in Tide Forecasting", Journal of Ocean Engineering, Vol. 29, No. 9, pp. 1003-1022, August 2002.
[12]	M. Campolo, A. Soldati and P. Andreussi, , "Artificial neural network approach to flood forecasting in the river Arno", Journal of Hydrological Sciences, 2003.
[13]	Tsong-Lin Lee "Back-propagation neural network for long-term tidal predictions" Journal of Ocean Engineering, Volume 31, Issue 2, pp. 225-238, February 2004.
[14]	Lee, T. L., C. P. Tsai and R. J. Shieh "Applied the Back-propagation Neural Network to Predict Long-Term Tidal Level" Asian Journal of Information Technology, Volume 5, Issue 4, pp. 396-401, 2006.
[15]	Ritu Vijay, and Rekha Govil "Tidal Data Analysis using ANN", Journal of World Academy of Science Engineering and Technology, 2006.
[16]	Bang-Fuh Chen, Han-Der Wang, Chih-Chun Chu " Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea", Journal of Ocean Engineering, Vol. 34, Issue 16, pp. 2161-2175, November 2007.
[17]	Jan F. Adamowski "River flow forecasting using wavelet and cross-wavelet transform models" Journal of Hydrological Processes, Volume 22, Number 25, pp. 4877–4891, 2008.
[18]	Remya, P.G., Kumar, Raj, and Basu Sujit, "Forecasting Tidal from Tidal levels using genetic algorithm"; Journal of Ocean Engineering, Volume 40, pp. 62-68, February 2012.
[19]	Burrage, D.M., C. R. Steinberg and K.P. Black "Predicting long-term currents in the Great Barrier Reef", 11th Australasian Conference on Coastal and Ocean Engineering, Australia, 1993.
[20]	William Mendenhall, Terry Sincich A Second Course in Statistics: Regression Analysis, Amazon, 2011.
[21]	Golberg, M. , Cho, H. A. "Introduction to Regression Analysis" , Southampton : Wit Press, 2003.
[22]	Rahman, S., Bhatnagar, R. "An expert system based algorithm for short term load forecast",IEEE Transaction on Power Systems, Volume 3, Page(s): 392 – 399, 1988.
[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.
[24]	S. A. Soliman ; A. M. Al-Kandaria; and M. E. El-Hawary "Time Domain Estimation Techniques for Harmonic Load Models" Journal of Electric Power Components and Systems, Volume 33, Number 10, 2005.
[25]	"Artificial Neural Networks" available online (August 2012), http://www.softcomputing.net/ann_chapter.pdf
[26]	Zurada, J., "Introduction to Neural Systems", West Publishing, 1992.
[27]	Ma L., Khorasani K. "New Training Strategies for Constructive Neural Networks with Application to Regression Problems" Neural Networks, Vol. 17, No. 4, May 2004.
[28]	T. Q. D. Khoa, L. M. Phuong, P.T.T.B inh, N. T. H. Lien "Application of Wavelet and Neural Network to Long-Term Load Forecasting" International Conference on Power System Technology , Singapore, 21-24 November 2004.
[29]	S. A. Soliman; A. M. Al-Kandaria; and M. E. El-Hawary "Time Domain Estimation Techniques for Harmonic Load Models" Journal of Electric Power Components and Systems, Volume 33, Number 10, 2005.
[30]	Atif S. Debs, "Modern Power Systems Control and Operation" Boston, Mass.: Kluwer Academic Publishers, 1988.
[31]	Hamed H. Aly "Forecasting, Modeling, and Control of Tidal currents Electrical Energy Systems"PhD thesis, Halifax, Canada. 2012.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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  -

Copy | Download

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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  -

Copy | Download