Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/1576
Title: Solar radiation forecasting by using deep neural networks in Eskisehir [Article]
Authors: Qasem, Mohammed
Başaran Filik, Ümmühan
Keywords: Daily global solar radiation forecasting
artificial neural network
deep neural network
renewable energy
Issue Date: 2021
Publisher: Yildiz Technical Univ
Abstract: According to the World Economic Outlook (WEO), the global demand for energy is presumably going to be increased due to growing the world's population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a result of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), relative root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R-2). Finally, simulation results provided that the best result is found by the DNN model.
URI: https://doi.org/10.14744/sigma.2021.00005
https://hdl.handle.net/20.500.13087/1576
ISSN: 1304-7205
1304-7191
Appears in Collections:Makine Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

Page view(s)

20
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.