Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System
Current Language EN
English English Türkçe Türkçe Español Español Português Português Pусский Pусский Filipino Filipino Tiếng Việt Tiếng Việt हिन्दी हिन्दी العربية العربية বাংলা বাংলা Deutsch Deutsch 中文 中文

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;Dehghani, Majid;Riahi-Madvar, Hossein;Hooshyaripor, Farhad;Mosavi, Amir;Shamshirband, Shahaboddin;Zavadskas, Edmundas Kazimieras;Chau, Kwok-wing;
energies 2019 Vol. 12 pp. 289-
284
dehghani2019energiesprediction

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

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

Access

DOI:
10.3390/en12020289
URL:
https://dx.doi.org/10.3390/en12020289

Citation

ID: 118372
Ref Key: dehghani2019energiesprediction
Use this key to autocite in SciMatic or Thesis Manager

References

References

No Bibliography

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
118372
Unique Identifier:
10.3390/en12020289
Network:
Scimatic Chain (ID: 481)
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet
Gas fees required in SCI Coins
Buy SCI

Saymatik Web3.0 Wallet

Google Play
App Store