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Title: Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system
Authors: Dehghani, M
RiahiMadvar, H
Hooshyaripor, F
Mosavi, A
Shamshirband, S
Zavadskas, EK
Chau, KW 
Keywords: Adaptive neuro-fuzzy inference system (ANFIS)
Artificial intelligence
Dam inflow
Deep learning
Energy system
Grey Wolf optimization (GWO)
Hybrid models
Hydrological modelling
Hydropower generation
Hydropower prediction
Machine learning
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Energies, 2019, v. 12, no. 2, 289 How to cite?
Journal: Energies 
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.
EISSN: 1996-1073
DOI: 10.3390/en12020289
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Dehghani M, Riahi-Madvar H, Hooshyaripor F, Mosavi A, Shamshirband S, Zavadskas EK, Chau K-W. Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System. Energies. 2019; 12(2):289 is available at
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