Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

Mohammed Qadem; Ümmühan Başaran Filik

Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

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 (R2). Finally, simulation results provided that the best result is found by the DNN model.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Mohammed Qadem This is me
0000-0002-4969-9495
Türkiye

Ümmühan Başaran Filik This is me
0000-0002-0715-821X
Türkiye

Publication Date

June 2, 2021

Submission Date

April 9, 2020

Acceptance Date

February 21, 2021

Published in Issue

Year 2021 Volume: 39 Number: 2

IZ

https://izlik.org/JA22UZ73CL

Cite

RIS / Bibtex

APA

Qadem, M., & Başaran Filik, Ü. (2021). Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r. Sigma Journal of Engineering and Natural Sciences, 39(2), 159-169. https://izlik.org/JA22UZ73CL

AMA

1.Qadem M, Başaran Filik Ü. Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r. SIGMA. 2021;39(2):159-169. https://izlik.org/JA22UZ73CL

Chicago

Qadem, Mohammed, and Ümmühan Başaran Filik. 2021. “Solar Radiation Forecasting by Using Deep Neural Networks in Eski̇şehi̇r”. Sigma Journal of Engineering and Natural Sciences 39 (2): 159-69. https://izlik.org/JA22UZ73CL.

EndNote

Qadem M, Başaran Filik Ü (June 1, 2021) Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r. Sigma Journal of Engineering and Natural Sciences 39 2 159–169.

IEEE

[1]M. Qadem and Ü. Başaran Filik, “Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r”, SIGMA, vol. 39, no. 2, pp. 159–169, June 2021, [Online]. Available: https://izlik.org/JA22UZ73CL

ISNAD

Qadem, Mohammed - Başaran Filik, Ümmühan. “Solar Radiation Forecasting by Using Deep Neural Networks in Eski̇şehi̇r”. Sigma Journal of Engineering and Natural Sciences 39/2 (June 1, 2021): 159-169. https://izlik.org/JA22UZ73CL.

JAMA

1.Qadem M, Başaran Filik Ü. Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r. SIGMA. 2021;39:159–169.

MLA

Qadem, Mohammed, and Ümmühan Başaran Filik. “Solar Radiation Forecasting by Using Deep Neural Networks in Eski̇şehi̇r”. Sigma Journal of Engineering and Natural Sciences, vol. 39, no. 2, June 2021, pp. 159-6, https://izlik.org/JA22UZ73CL.

Vancouver

1.Mohammed Qadem, Ümmühan Başaran Filik. Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r. SIGMA [Internet]. 2021 Jun. 1;39(2):159-6. Available from: https://izlik.org/JA22UZ73CL