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Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Author

Listed:
  • Majid Dehghani

    (Technical and Engineering Department, Faculty of Civil Engineering, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rafsanjan 7718897111, Iran)

  • Hossein Riahi-Madvar

    (College of Agriculture, Vali-e-Asr University of Rafsanjan, P.O. Box 518, Rafsanjan 7718897111, Iran)

  • Farhad Hooshyaripor

    (Technical and Engineering Department, Science and Research, Branch, Islamic Azad University, Tehran 1477893855, Iran)

  • Amir Mosavi

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam)

  • Edmundas Kazimieras Zavadskas

    (Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania)

  • Kwok-wing Chau

    (Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

Suggested Citation

  • Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:289-:d:198716
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    References listed on IDEAS

    as
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    2. Ozgur Kisi & Jalal Shiri, 2011. "Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3135-3152, October.
    3. Guolei Tang & Huicheng Zhou & Ningning Li & Feng Wang & Yajun Wang & Deping Jian, 2010. "Value of Medium-range Precipitation Forecasts in Inflow Prediction and Hydropower Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2721-2742, September.
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