Long-term wind energy prediction using three machine learning algorithms.
| dc.contributor.author | Deria, Reziq | |
| dc.contributor.author | Isead, Asma | |
| dc.date.accessioned | 2022-02-20T12:11:38Z | |
| dc.date.available | 2022-02-20T12:11:38Z | |
| dc.date.issued | 2020-05-22 | |
| dc.description.abstract | In this research, an approach for predicting wind energy in the long term has been developed based on daily average cubic wind speed and standard deviation. Three machine learning algorithms were used namely, Cascade-forward neural network (CFNN), random forests (RFs), and support vector machines (SVM). The R-Squared values were found to be 0.9996,0.9901, and 0.9991 and RMSE values 41.1659, 68.4101, and 205.10, respectively. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.11888/16798 | |
| dc.supervisor | Dr. Tamer Khatib | en_US |
| dc.title | Long-term wind energy prediction using three machine learning algorithms. | en_US |
| dc.type | Graduation project | en_US |