| dc.creator |
GÜLAY, EMRAH |
|
| dc.creator |
Sen, Mustafa |
|
| dc.creator |
AKGÜN, ÖMER BURAK |
|
| dc.date |
2024-01-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:20:36Z |
|
| dc.date.available |
2025-02-25T10:20:36Z |
|
| dc.identifier |
3f70b7b4-f628-4fd7-ab59-f691d37c25bb |
|
| dc.identifier |
10.1016/j.energy.2023.129566 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/3f70b7b4-f628-4fd7-ab59-f691d37c25bb/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99450 |
|
| dc.description |
When it comes to energy sources used in electricity production, the future forecasting of electricity production from renewable energy sources is highly important for both the success of technological advancements in the renewable energy field and energy security. To forecast electricity production from renewable energy sources reliably, it is necessary to accurately model the components of the relevant series. The central argument of this paper is that the various components derived from electricity production data, particularly the residual component, retain valuable predictive information despite their intricate and nonlinear nature. While linear modelling may be highly accurate initially, repeating residuals within linear structures is a discrepancy in terms of data type and methodology. In this paper, different types of hybrid models that combine a decomposition method and both machine learning and statistical approaches are suggested for forecasting electricity production from different energy sources. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models |
|
| dc.type |
info:eu-repo/semantics/article |
|