Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method
International Journal of Energy and Power Engineering
Volume 4, Issue 5, October 2015, Pages: 280-286
Received: Sep. 9, 2015;
Accepted: Sep. 26, 2015;
Published: Oct. 24, 2015
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Miguel Meque Uamusse, Department of Water Resources, Lund University, Lund, Sweden; Faculdade de Engenharia, Universidade Eduardo Mondlane, Maputo, Mozambique
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
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.
Miguel Meque Uamusse,
Frede de Oliveira Carvalho,
Monthly Stream Flow Prediction for Small Hydropower Plants in Pungwe River in Mozambique Using the Wavelet Method, International Journal of Energy and Power Engineering.
Vol. 4, No. 5,
2015, pp. 280-286.
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