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IJMLC 2020 Vol.10(2): 253-258 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.928

Estimation of the Monthly Global, Direct, and Diffuse Solar Radiation in Japan Using Artificial Neural Network

Adi Kurniawan and Eiji Shintaku

Abstract—In order to obtain the optimal design of solar energy system, the data of solar radiation should be provided. In this study, an estimation model of monthly solar radiation in Japan is developed using artificial neural network (ANN). The purpose of this study is to provide an accurate model to estimate the solar radiation, especially for the location where measured data is not available. The structure of ANN is constructed using geographical and 6 years-meteorological data between 2011-2016. The model has been validated by comparing the estimation results with measured solar radiation data on five different stations in 2017. Considering relatively small mean absolute percentage error (MAPE) and root mean square error (RMSE), it is believed that the proposed model could accurately predict the monthly solar radiation, which further could be used to obtain optimal design of solar energy system in Japan.

Index Terms—Artificial neural network, Japan, meteorological data, solar radiation estimation.

A. Kurniawan is with the Department of Marine Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia. Currently, he is also with the Department of Transportation and Environmental Systems, Hiroshima University, Higashi-Hiroshima 739-8511 Japan (e-mail: adi.kurniawan@ne.its.ac.id).
E. Shintaku is with the Department of Transportation and Environmental Systems, Hiroshima University, Higashi-Hiroshima 739-8511 Japan (email: eshin@hiroshima-u.ac.jp).

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Cite: Adi Kurniawan and Eiji Shintaku, "Estimation of the Monthly Global, Direct, and Diffuse Solar Radiation in Japan Using Artificial Neural Network," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 253-258, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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