General Information
    • ISSN: 1793-821X
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Jennifer Zeng
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Chemical Abstracts Services (CAS), Ulrich's Periodicals Directory, Google Scholar, ProQuest.
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Editor-in-chief
School of Science and Technology Universiti Malaysia Sabah, Malaysia.
I would like to express my appreciation to all authors, reviewers and edtors.
JOCET 2018 Vol.6(4): 333-338 ISSN: 1793-821X
DOI: 10.18178/JOCET.2018.6.4.484

Intelligent Prediction Model for Run-of-River Flow Considering Electricity Extreme Conditions

Raju Rai and Ken Nagasaka
Abstract—The Artificial neural networks (ANNs) is becoming a common analysis of hydrology and water resources development, management, modeling and prediction systems. Nepal is a developing country with rich in water resources, the electricity demand is very high but generation is very low. The river flow rate plays an increasingly important role in electricity generation in Nepal. To reduce the power shortage in a local community, prediction of river flow is most necessary for the Run-of-River hydropower plants in Nepal. In this research, the river flow forecasting model based on the Artificial Neural Networks (ANNs) was developed using the Neural Connection. The performance of the developed model based on the results of this research, prediction of river flow was observed. One week of flow prediction test was conducted and one week ahead of its hydropower generation potential was identified. Employing Radial Basis Function Network (RBFN) method for forecasting of river flow and observed less than 8% of error of test data for one week. It has been analyzed that river flow rate prediction helps to reduce the demand for electric power and generation of hydropower plants. The prediction method optimizes and plan for the future system. The paper analyzes the river flow prediction and technical potential of electricity generation of the hydropower plant.

Index Terms—Artificial neural network, prediction, power shortage, run-of-river, hydropower plant, RBFN, Nepal.

Raju Rai is with the Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan (e-mail: raj_shang@hotmail.com).
Ken Nagasaka is with the Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan (e-mail: bahman@cc.tuat.ac.jp).

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Cite:Raju Rai and Ken Nagasaka, "Intelligent Prediction Model for Run-of-River Flow Considering Electricity Extreme Conditions," Journal of Clean Energy Technologies vol. 6, no. 4, pp. 333-338, 2018.

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