Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island

  • Syamsul Bahri Universitas Mataram

Abstract

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.

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References

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Published
2020-12-22
How to Cite
BAHRI, Syamsul. Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 11, n. 3, p. 178-187, dec. 2020. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/65389>. Date accessed: 27 feb. 2021. doi: https://doi.org/10.24843/LKJITI.2020.v11.i03.p06.