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 and Kwok-wing Chau
Additional contact information
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
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, 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
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2019, vol. 12, issue 2, 1-20

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.

Keywords: hydropower generation; hydropower prediction; dam inflow; machine learning; hybrid models; artificial intelligence; prediction; grey wolf optimization (GWO); deep learning; adaptive neuro-fuzzy inference system (ANFIS); hydrological modelling; hydroinformatics; energy system; drought; forecasting; precipitation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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