One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models
This paper proposes two hybrid grey-based short-term wind power prediction techniques: GM(1,1)-ARMA and GM(1,1)-NARnet. These techniques are combined with ARMA models and nonlinear autoregressive neural network (NARnet) models, respectively. The efficiency of these algorithms is examined using a rec...
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2016
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| Online Access: | http://hdl.handle.net/11073/16312 |
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| _version_ | 1864513432330436608 |
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| author | Osman, Ahmed |
| author2 | Hassan, Mohamed Marzbani, Fatemeh Landolsi, Taha |
| author2_role | author author author |
| author_facet | Osman, Ahmed Hassan, Mohamed Marzbani, Fatemeh Landolsi, Taha |
| author_role | author |
| dc.creator.none.fl_str_mv | Osman, Ahmed Hassan, Mohamed Marzbani, Fatemeh Landolsi, Taha |
| dc.date.none.fl_str_mv | 2016 2018-11-05T08:17:12Z 2018-11-05T08:17:12Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Osman, Ahmed, Mohamed S. Hassan, Fatemeh Marzabani, and Taha Landolsi. "One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models." International Journal of Operational Research 27, no. 1-2 (2016): 212-231. 1745-7653 http://hdl.handle.net/11073/16312 10.1504/IJOR.2016.078472 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Inderscience |
| dc.relation.none.fl_str_mv | International Journal of Operational Research https://doi.org/10.1504/IJOR.2016.078472 |
| dc.subject.none.fl_str_mv | Wind power forecasting Wind energy prediction Time series analysis ARMA models Grey theory GM(1,1) GM(1,1)-ARMA GM(1,1)-NARnet neural networks |
| dc.title.none.fl_str_mv | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper proposes two hybrid grey-based short-term wind power prediction techniques: GM(1,1)-ARMA and GM(1,1)-NARnet. These techniques are combined with ARMA models and nonlinear autoregressive neural network (NARnet) models, respectively. The efficiency of these algorithms is examined using a recorded wind power dataset. The performance of these predictors is compared with classical ARMA models as well as the traditional grey model GM(1,1). Unlike the classical predictors, the proposed hybrid algorithms are not affected by the inherent uncertainty in the wind power. Therefore, the results obtained using the proposed hybrid algorithms outperform those obtained using classical predictors. In contrast to the GM(1,1)-ARMA model, the GM(1,1)-NARnet model utilises the nonlinear components of wind power in the forecasting procedure. Consequently, the obtained results from the GM(1,1)-NARnet outperform those obtained by the GM(1,1)-ARMA. |
| format | article |
| id | aus_2aae82b19cf465047d662462b3bfcf1a |
| identifier_str_mv | Osman, Ahmed, Mohamed S. Hassan, Fatemeh Marzabani, and Taha Landolsi. "One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models." International Journal of Operational Research 27, no. 1-2 (2016): 212-231. 1745-7653 10.1504/IJOR.2016.078472 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/16312 |
| publishDate | 2016 |
| publisher.none.fl_str_mv | Inderscience |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey ModelsOsman, AhmedHassan, MohamedMarzbani, FatemehLandolsi, TahaWind power forecastingWind energy predictionTime series analysisARMA modelsGrey theoryGM(1,1)GM(1,1)-ARMAGM(1,1)-NARnetneural networksThis paper proposes two hybrid grey-based short-term wind power prediction techniques: GM(1,1)-ARMA and GM(1,1)-NARnet. These techniques are combined with ARMA models and nonlinear autoregressive neural network (NARnet) models, respectively. The efficiency of these algorithms is examined using a recorded wind power dataset. The performance of these predictors is compared with classical ARMA models as well as the traditional grey model GM(1,1). Unlike the classical predictors, the proposed hybrid algorithms are not affected by the inherent uncertainty in the wind power. Therefore, the results obtained using the proposed hybrid algorithms outperform those obtained using classical predictors. In contrast to the GM(1,1)-ARMA model, the GM(1,1)-NARnet model utilises the nonlinear components of wind power in the forecasting procedure. Consequently, the obtained results from the GM(1,1)-NARnet outperform those obtained by the GM(1,1)-ARMA.Inderscience2018-11-05T08:17:12Z2018-11-05T08:17:12Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfOsman, Ahmed, Mohamed S. Hassan, Fatemeh Marzabani, and Taha Landolsi. "One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models." International Journal of Operational Research 27, no. 1-2 (2016): 212-231.1745-7653http://hdl.handle.net/11073/1631210.1504/IJOR.2016.078472en_USInternational Journal of Operational Researchhttps://doi.org/10.1504/IJOR.2016.078472oai:repository.aus.edu:11073/163122024-08-22T12:16:37Z |
| spellingShingle | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models Osman, Ahmed Wind power forecasting Wind energy prediction Time series analysis ARMA models Grey theory GM(1,1) GM(1,1)-ARMA GM(1,1)-NARnet neural networks |
| status_str | publishedVersion |
| title | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| title_full | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| title_fullStr | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| title_full_unstemmed | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| title_short | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| title_sort | One-Hour-Ahead Wind Power Forecast Using Hybrid Grey Models |
| topic | Wind power forecasting Wind energy prediction Time series analysis ARMA models Grey theory GM(1,1) GM(1,1)-ARMA GM(1,1)-NARnet neural networks |
| url | http://hdl.handle.net/11073/16312 |