Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/80674
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Civil and Environmental Engineering | - |
dc.creator | Dehghani, M | - |
dc.creator | RiahiMadvar, H | - |
dc.creator | Hooshyaripor, F | - |
dc.creator | Mosavi, A | - |
dc.creator | Shamshirband, S | - |
dc.creator | Zavadskas, EK | - |
dc.creator | Chau, KW | - |
dc.date.accessioned | 2019-04-23T08:16:52Z | - |
dc.date.available | 2019-04-23T08:16:52Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/80674 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.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 (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | 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 https://doi.org/10.3390/en12020289 | en_US |
dc.subject | Adaptive neuro-fuzzy inference system (ANFIS) | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Dam inflow | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Drought | en_US |
dc.subject | Energy system | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Grey Wolf optimization (GWO) | en_US |
dc.subject | Hybrid models | en_US |
dc.subject | Hydroinformatics | en_US |
dc.subject | Hydrological modelling | en_US |
dc.subject | Hydropower generation | en_US |
dc.subject | Hydropower prediction | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Precipitation | en_US |
dc.subject | Prediction | en_US |
dc.title | Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.3390/en12020289 | en_US |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Energies, 2019, v. 12, no. 2, 289 | - |
dcterms.isPartOf | Energies | - |
dcterms.issued | 2019 | - |
dc.identifier.scopus | 2-s2.0-85060517286 | - |
dc.identifier.eissn | 1996-1073 | en_US |
dc.identifier.artn | 289 | en_US |
dc.description.validate | 201904 bcma | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Dehghani_Prediction_hydropower_generation.pdf | 7.4 MB | Adobe PDF | View/Open |
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