Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
| Autor: | Muhammed SÜTÇÜ, Kübra Nur ŞAHİN, Yunus KOLOĞLU, Mevlüt Emirhan ÇELİKEL, İbrahim Tümay GÜLBAHAR |
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| Přispěvatelé: | AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Sütçü, Muhammed, Şahin, Kübra Nur, Koloğlu, Yunus, Çelikel, Mevlüt Emirhan, Gülbahar, İbrahim Tümay |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Volume: 26, Issue: 1 91-104 Sakarya University Journal of Science |
| ISSN: | 2147-835X |
| Popis: | Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other. |
| Databáze: | OpenAIRE |
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