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

Miguel Meque Uamusse; Petro Ndalila; Alberto JúlioTsamba; Frede de Oliveira Carvalho; Kenneth Person

doi:doi:10.11648/j.ijepe.20150405.17

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

Miguel Meque Uamusse, Petro Ndalila, Alberto JúlioTsamba, Frede de Oliveira Carvalho, Kenneth Person

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 5)

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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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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TY  - JOUR
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
N1  - https://doi.org/10.11648/j.ijepe.20150405.17
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
SP  - 280
EP  - 286
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
IS  - 5
ER  -

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Author Information

Miguel Meque Uamusse

Department of Water Resources, Lund University, Lund, Sweden
Petro Ndalila

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

Faculdade de Engenharia, Universidade Eduardo Mondlane, Maputo, Mozambique
Frede de Oliveira Carvalho

Departamento de Engenharia Química, Universidade Federal de Alagoas, Brazil
Kenneth Person

Department of Water Resources, Lund University, Lund, Sweden

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Plain Text BibTeX RIS

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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TY  - JOUR
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
N1  - https://doi.org/10.11648/j.ijepe.20150405.17
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
SP  - 280
EP  - 286
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
IS  - 5
ER  -

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