Hybrid analysis of photovoltaic energy data containing excessive structural zeros
Yazarlar (5)
Doç. Dr. Yasin ALTINIŞIK Sinop Üniversitesi, Türkiye
Doç. Dr. Demet HAN AYDIN Sinop Üniversitesi, Türkiye
Dr. Öğr. Üyesi Vedat Esen İstanbul Topkapı Üniversitesi, Türkiye
Taner Dindar Ankara Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ali Samet Sarkın Osmaniye Korkut Ata Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Scientific Reports (Q1) | ||
| Dergi ISSN | 2045-2322 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2026 |
| Cilt / Sayı / Sayfa | 16 / 1 / 28553–28553 | DOI | 10.1038/S41598-026-58719-0 |
| Makale Linki | https://doi.org/10.1038/s41598-026-58719-0 | ||
| UAK Araştırma Alanları |
İstatistiksel Analiz
Teorik İstatistik
Uygulamalı İstatistik
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| Özet |
| Photovoltaic (PV) power generation plays a critical role in the global transition toward sustainable energy systems. However, accurate PV power forecasting remains challenging due to the non-stationary nature of PV power time series and the presence of structural zeros. Most existing studies rely on parametric models or treat zero inflation as a secondary issue. In this study, a hybrid hurdle modeling framework is implemented to explicitly account for both structural zeros and continuous positive power values through a two-part structure. The zero component is modeled using Bayesian logistic regression (BLR), random forest classifier (RFC), and support vector classifier (SVC). The positive values are analyzed using the parametric models Gamma regression (GR), Log-normal regression (LNR), and Weibull regression (WR) and the non-parametric machine learning (ML) techniques random forest regression (RFR ... |
| Anahtar Kelimeler |
| Hurdle models | Hybrid modeling | Machine learning | Photovoltaic power forecasting | Structural zeros |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |