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Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method

Received: 9 September 2015     Accepted: 26 September 2015     Published: 24 October 2015
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Abstract

The effects of a discrete wavelet-transformation data-preprocessing method on neural-network-based monthly streamflow prediction models in producing energy from small hydro power plants in the Pungwe River basin in Mozambique were investigated. Data from a Vanduzi gauging station in Pungwe River basin were collected. Eight different single-step-ahead monthly stream flow neural prediction models were developed. Coupled simulation involving use of MATLAB and of a Wavelet-Neural Network was employed. Different models were tested using the same sample in each case, an Artificial Neural Network (ANN) being found to performance best. The major objective of the research project was to analyze the monthly stream flow predictions in the Pungwe River, to be able to make as appropriate decisions as possible during dry or wet spells, and also to resolve as effectively as possible conflicts regarding water resourses.

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 5)
DOI 10.11648/j.ijepe.20150405.17
Page(s) 280-286
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), 2015. Published by Science Publishing Group

Keywords

Renewable Energy, Hydropower, Wavelet Artificial Neural Network, Monthly Flow Prediction

References
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[17] Nejad, F. H., and Nourani, V. (2012). “Elevation of wavelet denoising performance via an ANN-based streamflow forecasting model.” International Journal of Computer Science and Management Research, Vol. 1, Issue 4, pp. 764-770.
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[21] Nourani, V., Baghanam, A.H., Adamowski, J., Gebremichael, M., 2013. Using selforganizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. Journal of Hydrology 476, 228–243.
[22] Dibike, Y.B., Solomatine, D.P., 2001. River flow forecasting using artificial neural networks. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26, 1–7.
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Cite This Article
  • APA Style

    Miguel Meque Uamusse, Petro Ndalila, Alberto JúlioTsamba, Frede de Oliveira Carvalho, Kenneth Person. (2015). Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method. International Journal of Energy and Power Engineering, 4(5), 280-286. https://doi.org/10.11648/j.ijepe.20150405.17

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    ACS Style

    Miguel Meque Uamusse; Petro Ndalila; Alberto JúlioTsamba; Frede de Oliveira Carvalho; Kenneth Person. Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method. Int. J. Energy Power Eng. 2015, 4(5), 280-286. doi: 10.11648/j.ijepe.20150405.17

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    AMA Style

    Miguel Meque Uamusse, Petro Ndalila, Alberto JúlioTsamba, Frede de Oliveira Carvalho, Kenneth Person. Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method. Int J Energy Power Eng. 2015;4(5):280-286. doi: 10.11648/j.ijepe.20150405.17

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  • @article{10.11648/j.ijepe.20150405.17,
      author = {Miguel Meque Uamusse and Petro Ndalila and Alberto JúlioTsamba and Frede de Oliveira Carvalho and Kenneth Person},
      title = {Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {5},
      pages = {280-286},
      doi = {10.11648/j.ijepe.20150405.17},
      url = {https://doi.org/10.11648/j.ijepe.20150405.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20150405.17},
      abstract = {The effects of a discrete wavelet-transformation data-preprocessing method on neural-network-based monthly streamflow prediction models in producing energy from small hydro power plants in the Pungwe River basin in Mozambique were investigated. Data from a Vanduzi gauging station in Pungwe River basin were collected. Eight different single-step-ahead monthly stream flow neural prediction models were developed. Coupled simulation involving use of MATLAB and of a Wavelet-Neural Network was employed. Different models were tested using the same sample in each case, an Artificial Neural Network (ANN) being found to performance best. The major objective of the research project was to analyze the monthly stream flow predictions in the Pungwe River, to be able to make as appropriate decisions as possible during dry or wet spells, and also to resolve as effectively as possible conflicts regarding water resourses.},
     year = {2015}
    }
    

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    T1  - Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method
    AU  - Miguel Meque Uamusse
    AU  - Petro Ndalila
    AU  - Alberto JúlioTsamba
    AU  - Frede de Oliveira Carvalho
    AU  - Kenneth Person
    Y1  - 2015/10/24
    PY  - 2015
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    DO  - 10.11648/j.ijepe.20150405.17
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
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    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.20150405.17
    AB  - The effects of a discrete wavelet-transformation data-preprocessing method on neural-network-based monthly streamflow prediction models in producing energy from small hydro power plants in the Pungwe River basin in Mozambique were investigated. Data from a Vanduzi gauging station in Pungwe River basin were collected. Eight different single-step-ahead monthly stream flow neural prediction models were developed. Coupled simulation involving use of MATLAB and of a Wavelet-Neural Network was employed. Different models were tested using the same sample in each case, an Artificial Neural Network (ANN) being found to performance best. The major objective of the research project was to analyze the monthly stream flow predictions in the Pungwe River, to be able to make as appropriate decisions as possible during dry or wet spells, and also to resolve as effectively as possible conflicts regarding water resourses.
    VL  - 4
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    ER  - 

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Author Information
  • Department of Water Resources, Lund University, Lund, Sweden

  • Department of Mechanical Engineering, Mbeya University of Science and Technology, Mbea, Tanzania

  • Faculdade de Engenharia, Universidade Eduardo Mondlane, Maputo, Mozambique

  • Departamento de Engenharia Química, Universidade Federal de Alagoas, Brazil

  • Department of Water Resources, Lund University, Lund, Sweden

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