Time Series Installed Capacity Forecasting with Deep Learning Approach for Türkiye
| dc.contributor.author | Altıparmak, Zeynep | |
| dc.contributor.author | Aksu, İnayet Özge | |
| dc.date.accessioned | 2025-01-06T17:22:54Z | |
| dc.date.available | 2025-01-06T17:22:54Z | |
| dc.date.issued | 2024 | |
| dc.department | Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi | |
| dc.description.abstract | Deep learning methods have been developed to solve different problems due to the complex nature of real-world problems. Accurate future forecasting of a country's installed capacity is also crucial for developing a good energy sustainability strategy for the country. In this paper, three different time series forecasting methods are used for forward forecasting of installed capacity: Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Installed power values for the years 1923-2021 were used in the study. Then, future forecasts are made until 2030. The GRU model achieved the best RMSE in the testing phase compared to the LSTM and CNN models. Although CNN is successful during training, it has a higher RMSE during testing compared to GRU. While all models predict a potential increase in electricity capacity by 2030, GRU and LSTM predict a more significant increase up to this point compared to CNN. | |
| dc.identifier.doi | 10.21605/cukurovaumfd.1560142 | |
| dc.identifier.endpage | 718 | |
| dc.identifier.issn | 2757-9255 | |
| dc.identifier.issue | 3 | |
| dc.identifier.startpage | 709 | |
| dc.identifier.trdizinid | 1269797 | |
| dc.identifier.uri | https://doi.org/10.21605/cukurovaumfd.1560142 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1269797 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/543 | |
| dc.identifier.volume | 39 | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.language.iso | en | |
| dc.relation.ispartof | Çukurova Üniversitesi Mühendislik Fakültesi dergisi | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_20241211 | |
| dc.subject | Deep learning | |
| dc.subject | Time series | |
| dc.subject | Future prediction | |
| dc.subject | Installed capacity | |
| dc.title | Time Series Installed Capacity Forecasting with Deep Learning Approach for Türkiye | |
| dc.type | Article |
