IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v22y2008i6p757-774.html
   My bibliography  Save this article

Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam

Author

Listed:
  • Murat Cobaner
  • Tefaruk Haktanir
  • Ozgur Kisi

Abstract

Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C·Q 2 . Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg–Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • Murat Cobaner & Tefaruk Haktanir & Ozgur Kisi, 2008. "Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 757-774, June.
    • Handle: RePEc:spr:waterr:v:22:y:2008:i:6:p:757-774
    DOI: 10.1007/s11269-007-9190-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-007-9190-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-007-9190-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
    3. Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
    4. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    5. V. Chandramouli & Paresh Deka, 2005. "Neural Network Based Decision Support Model for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 447-464, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kucukali, Serhat & Al Bayatı, Omar & Maraş, H. Hakan, 2021. "Finding the most suitable existing irrigation dams for small hydropower development in Turkey: A GIS-Fuzzy logic tool," Renewable Energy, Elsevier, vol. 172(C), pages 633-650.
    2. Muhammad Azmat & Francesco Laio & Davide Poggi, 2015. "Estimation of Water Resources Availability and Mini-Hydro Productivity in High-Altitude Scarcely-Gauged Watershed," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5037-5054, November.
    3. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    4. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    5. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    2. Maya Rajnarayan Ray & Arup Kumar Sarma, 2016. "Influence of Time Discretization and Input Parameter on the ANN Based Synthetic Streamflow Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4695-4711, October.
    3. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    4. Yi-min Wang & Jian-xia Chang & Qiang Huang, 2010. "Simulation with RBF Neural Network Model for Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2597-2610, September.
    5. L. Karthikeyan & D. Kumar & Didier Graillot & Shishir Gaur, 2013. "Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 871-883, February.
    6. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    7. Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
    8. Maria Diamantopoulou & Vassilis Antonopoulos & Dimitris Papamichail, 2007. "Cascade Correlation Artificial Neural Networks for Estimating Missing Monthly Values of Water Quality Parameters in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 649-662, March.
    9. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    10. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    11. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    12. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    13. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    14. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    15. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    16. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    17. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    18. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    19. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    20. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:22:y:2008:i:6:p:757-774. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.