Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü, Türkiye
Tezin Onay Tarihi: 2020
Öğrenci: Kadir Özen
Danışman: DİLEM YILDIRIM KASAP
Özet:Fundamental dynamics behind electricity prices are multi-dimensional and elaborate. A popular approach to forecasting electricity price is to utilize large number of predictors. In this study, using the day-ahead electricity price data from commonly studied markets of five major series and GEFCom2014 data, a variant of shrinkage method, Bootstrap Aggregation (bagging) is proposed to incorporate information from available predictors. Bagging manifests itself as a computationally simpler alternative to commonly used Least Absolute Shrinkage and Selection Operator (lasso) in multivariate EPF context and even shows superior forecasting ability in some markets. Moreover, considering the significant dependence of intra-day electricity prices, we also propose factor augmentation to exploit this dependence. The inclusion of latent factors, selected via Bayesian Information Criterion, improves ability to forecast in multivariate modeling framework and in some cases even outperform sophisticated shrinkage methods as measured by the Diebold-Mariano test.