Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system

Majid Dehghani, Hossein Riahi-Madvar, Farhad Hooshyaripor, Amir Mosavi, Shahaboddin Shamshirband, Edmundas Kazimieras Zavadskas, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

147 Citations (Scopus)


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.

Original languageEnglish
Article number289
Issue number2
Publication statusPublished - 17 Jan 2019


  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Artificial intelligence
  • Dam inflow
  • Deep learning
  • Drought
  • Energy system
  • Forecasting
  • Grey Wolf optimization (GWO)
  • Hybrid models
  • Hydroinformatics
  • Hydrological modelling
  • Hydropower generation
  • Hydropower prediction
  • Machine learning
  • Precipitation
  • Prediction

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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