| Yazarlar (2) |
Ashraf Mohammed Abusida,
|
Prof. Dr. Aybaba HANÇERLİOĞULLARI
Türkiye |
| Özet |
| The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising … |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY |
| Dergi ISSN | 1738-7906 Wos Dergi |
| Dergi Tarandığı Indeksler | ESCI |
| Makale Dili | Türkçe |
| Basım Tarihi | 03-2022 |
| Cilt No | 22 |
| Sayı | 3 |
| Sayfalar | 220 / 228 |
| Doi Numarası | 10.22937/IJCSNS.2022.22.3.28 |
| Makale Linki | https://www.webofscience.com/wos/woscc/full-record/WOS:000766697800003 |
| Atıf Sayıları | |
| Google Scholar | 2 |
