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DAĞİSTANLI Ö., Erbay H., Yurttakal A. H., Kor H.

Thermal Science, vol.26, no.4, pp.2895-2906, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.2298/tsci2204895d
  • Journal Name: Thermal Science
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Directory of Open Access Journals
  • Page Numbers: pp.2895-2906
  • Keywords: solar irradiation, deep learning, estimation, GLOBAL HORIZONTAL IRRADIANCE, RENEWABLE ENERGY, RADIATION, MODEL, DIFFUSE, DNI
  • Yozgat Bozok University Affiliated: Yes


© 2022. Society of Thermal Engineers of SerbiaGlobal solar irradiation data is a crucial component to measure solar energy potential when we plan, size, and design solar photovoltaic fields. Often, due to the absence of measuring equipment at meteorological stations, data for the place of interest are not available. However, solar irradiation can be estimated by ordinary meteorological data such as humidity, and air temperature. Herein we propose two different deep learning methods, one based on a deep neural network regression and the other based on multivariate long short term memory unit networks, to estimate solar irradiation at given locations. Validation criteria include mean absolute error, mean squared error, and coefficient of determination (R2 value). According to the simulation results, multivariate long short term memory unit networks performs slightly better than deep neural network. Even though both have very close R2 values, multivariate long short term memory’s R2 values are more consistent. The same is true for mean squared error and mean absolute error