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

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ID: 118372
2019
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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.
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dehghani2019energiesprediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Majid Dehghani;Hossein Riahi-Madvar;Farhad Hooshyaripor;Amir Mosavi;Shahaboddin Shamshirband;Edmundas Kazimieras Zavadskas;Kwok-wing Chau;Dehghani, Majid;Riahi-Madvar, Hossein;Hooshyaripor, Farhad;Mosavi, Amir;Shamshirband, Shahaboddin;Zavadskas, Edmundas Kazimieras;Chau, Kwok-wing;
Journal energies
Year 2019
DOI
10.3390/en12020289
URL
https://dx.doi.org/10.3390/en12020289
Keywords
Deep learning artificial intelligence Machine learning drought prediction hydropower generation precipitation hybrid models forecasting grey wolf optimization (gwo) hydrological modelling energy system hydropower prediction dam inflow adaptive neuro-fuzzy inference system (anfis) hydroinformatics

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