dc.contributor.author | Dehghani, Majid | |
dc.contributor.author | Riahi-Madvar, Hossein | |
dc.contributor.author | Hooshyaripor, Farhad | |
dc.contributor.author | Mosavi, Amir | |
dc.contributor.author | Shamshirband, Shahaboddin | |
dc.contributor.author | Zavadskas, Edmundas Kazimieras | |
dc.contributor.author | Chau, Kwok-wing | |
dc.date.accessioned | 2023-09-18T17:29:51Z | |
dc.date.available | 2023-09-18T17:29:51Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 1996-1073 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/123822 | |
dc.description.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. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-20 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | RePec | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | Agris | |
dc.relation.isreferencedby | AGORA | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.source.uri | https://www.mdpi.com/1996-1073/12/2/289/htm | |
dc.source.uri | https://doi.org/10.3390/en12020289 | |
dc.title | Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 36 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vali-e-Asr University of Rafsanjan | |
dc.contributor.institution | Islamic Azad University | |
dc.contributor.institution | Obuda University Oxford Brookes University | |
dc.contributor.institution | Ton Duc Thang University | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Hong Kong Polytechnic University | |
dc.contributor.faculty | Statybos fakultetas / Faculty of Civil Engineering | |
dc.contributor.department | Tvariosios statybos institutas / Institute of Sustainable Construction | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.researchfield | T 002 - Statybos inžinerija / Construction and engineering | |
dc.subject.vgtuprioritizedfields | SD0404 - Statinių skaitmeninis modeliavimas ir tvarus gyvavimo ciklas / BIM and Sustainable lifecycle of the structures | |
dc.subject.ltspecializations | L102 - Energetika ir tvari aplinka / Energy and a sustainable environment | |
dc.subject.en | adaptive neuro-fuzzy inference system (ANFIS) | |
dc.subject.en | artificial intelligence | |
dc.subject.en | dam inflow | |
dc.subject.en | deep learning | |
dc.subject.en | drought | |
dc.subject.en | energy system | |
dc.subject.en | forecasting | |
dc.subject.en | Grey Wolf optimization (GWO) | |
dc.subject.en | hybrid models | |
dc.subject.en | hydroinformatics | |
dc.subject.en | hydrological modelling | |
dc.subject.en | hydropower generation | |
dc.subject.en | hydropower prediction | |
dc.subject.en | machine learning | |
dcterms.sourcetitle | Energies | |
dc.description.issue | iss. 2 | |
dc.description.volume | vol. 12 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 2-s2.0-85060517286 | |
dc.identifier.doi | 000459743700090 | |
dc.identifier.doi | 10.3390/en12020289 | |
dc.identifier.elaba | 34382346 | |