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
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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.
Renewable Energy, Hydropower, Wavelet Artificial Neural Network, Monthly Flow Prediction
To cite this article
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, International Journal of Energy and Power Engineering. Vol. 4, No. 5, 2015, pp. 280-286. doi: 10.11648/j.ijepe.20150405.17
EDM ( Electricidad de Mocambique), 2015. Stastitical Summary.
IEA, 2009. IEA Energy Statistics - Energy Balances for Mozambique. Available at: Accessed May, 2015].
Cuamba B.C., Uthui R. Chenene M.L. et al. (unpubl.) Identification of areas with likely good wind regimes for energy applications in Mozambique. Eduardo Mondlane University, Maputo
Santos, C. A. G., Silva, G. B. L., (2014) Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Journal des Sciences Hydrologiques, 59 (2) 312–324.
Solgi, A., Radmanesh, F., Zarei, H., Nourani, V., (2014), Hybrid Models Performance Assessment to Predict Flow of Gamasyab River International journal of Advanced Biological and Biomedical Research Volume 2, Issue 5, 2014: 1837-1846.
O. Kisi.,(2008). “Stream flow forecasting using neuro-wavelet technique,” Hydrological Processes, vol. 22, no. 20, 4142–4152.
FUNAE (Fundo de Energia), 2015. Annual Report.
V. Nourani, M. T. Alami, and M. H. Aminfar, (2009), “A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation,” Engineering Applications of Artificial Intelligence, Vol. 22, no. 3, 466–472.
Nourani V, Hosseini Baghanam A, Adamowski J, Gebremichael M, (2013), Using self-organizingmaps and wavelet transforms for space–time preprocessing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J Hydrol 476:228–243.
Sreekanth, P., Geethanjali, D.N., Sreedevi, P.D., Ahmed, S., Kumar, N.R., Jayanthi, P.D.K., (2009), Forecasting groundwater level using artificial neural networks. Current Science 96 (7), 933–939.
Mohammadi, K., (2008), Groundwater table estimation using MODFLOW and artificial neural networks. Water Science and Technology Library 68 (2), 127– 138.
Nourani, V., Hosseini, A., Adamowski, J., Kisi,O, (2014), Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review, Journal of Hydrology 514 , 358–377.
Partal, T., Kisi, Ö. (2007), Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Jornal of Hydrology, 342,199-212.
Krishna, B.; Satyaji Rao, Y. R., Naya, P.C. (2011) Times Series Modeling of River Flow Using Wavelet Neural Networks, Journal of water resource and protection, 3, 50-59.
Nayak P.C., Venkatesh B., Krishna, B., Sharad, K J., (2013) Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. Journal of Hydrology 493 57–67.
Rezaeianzadeh, M., Tabari, H, Yazdi, A. A., Isik, S., and Kalin, L. (2014). “Flood flow forecasting using ANN, ANFIS and regression models.” Neural Computing and Applications, Vol. 25, Issue 1, pp. 25-37, DOI: 10.1007/s00521-013-1443-6.
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.
Kisi, O. (2006). “Streamflow forecasting using different artificial neural network algorithms.” Journal of Hydrologic Engineering, Vol. 12, Issue 5, pp. 532-539, DOI: 10.1061/(ASCE)1084-0699(2007)12: 5(532).
Kim, T.W., Valdes, J.B., 2003. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering 6, 319–328.
Nourani, V., Kisi, Ö., Komasi, M., 2011. Two hybrid artificial intelligence approaches for modeling rainfall-runoff process. Journal of Hydrology 402, 41–59.
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.
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.
Badrzadeh, H., Sarukkalige R., Jayawardena,A.W., 2013. Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting. Journal of Hydrology 507,75–85.
Jang, J.S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23, 665.
Chiu, S., 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems 2.
Uamusse, M., Persson, K. and Tsamba, A. (2014) Gasification of Cashew Nut Shell Using Gasifier Stovein Mozambique. Journal of Power and Energy Engineering, 2, 11-18. doi: 10.4236/jpee.2014.27002.
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