Dynamic Neural Network Model Design for Solar Radiation Forecast

  • Syamsul Bahri Universitas Mataram
  • Muhammad Rijal Alfian Department of Mathematics, Faculty of Mathematics and Sciences, University of Mataram Mataram, Indonesia
  • Nurul Fitriyani Department of Mathematics, Faculty of Mathematics and Sciences, University of Mataram Mataram, Indonesia

Abstract

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.

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Published
2022-08-24
How to Cite
BAHRI, Syamsul; ALFIAN, Muhammad Rijal; FITRIYANI, Nurul. Dynamic Neural Network Model Design for Solar Radiation Forecast. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 13, n. 2, p. 96-104, aug. 2022. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/80433>. Date accessed: 29 sep. 2022. doi: https://doi.org/10.24843/LKJITI.2022.v13.i02.p03.