MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Kübra Kaysal; Fatih Onur Hocaoğlu

doi:10.18038/estubtda.1184918

EN TR

MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Abstract

IIn recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.

Keywords

Artificial neural networks, Wavelet transform, Mother wavelets, Solar radiation estimation.

MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Abstract

In recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.

Keywords

Artificial neural networks, Wavelet transform, Mother wavelets, Solar radiation estimation

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Kübra Kaysal ^*
0000-0003-3983-2608
Türkiye

Fatih Onur Hocaoğlu
0000-0002-3640-7676
Türkiye

Publication Date

June 21, 2023

Submission Date

October 5, 2022

Acceptance Date

June 12, 2023

Published in Issue

Year 2023 Volume: 24 Number: 2

DOI

https://doi.org/10.18038/estubtda.1184918

IZ

https://izlik.org/JA55PD76AB

APA

Kaysal, K., & Hocaoğlu, F. O. (2023). MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 24(2), 141-154. https://doi.org/10.18038/estubtda.1184918

AMA

1.Kaysal K, Hocaoğlu FO. MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Estuscience - Se. 2023;24(2):141-154. doi:10.18038/estubtda.1184918

Chicago

Kaysal, Kübra, and Fatih Onur Hocaoğlu. 2023. “MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 (2): 141-54. https://doi.org/10.18038/estubtda.1184918.

EndNote

Kaysal K, Hocaoğlu FO (June 1, 2023) MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 2 141–154.

IEEE

[1]K. Kaysal and F. O. Hocaoğlu, “MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION”, Estuscience - Se, vol. 24, no. 2, pp. 141–154, June 2023, doi: 10.18038/estubtda.1184918.

ISNAD

Kaysal, Kübra - Hocaoğlu, Fatih Onur. “MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24/2 (June 1, 2023): 141-154. https://doi.org/10.18038/estubtda.1184918.

JAMA

1.Kaysal K, Hocaoğlu FO. MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Estuscience - Se. 2023;24:141–154.

MLA

Kaysal, Kübra, and Fatih Onur Hocaoğlu. “MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 24, no. 2, June 2023, pp. 141-54, doi:10.18038/estubtda.1184918.

Vancouver

1.Kübra Kaysal, Fatih Onur Hocaoğlu. MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION. Estuscience - Se. 2023 Jun. 1;24(2):141-54. doi:10.18038/estubtda.1184918

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