Assessment of solar data estimation models for four cities in Iran

Date

2015-04-29

Author

Jahani, Elham
Sadati, S. M. Sajed
Yousefzadeh, Moslem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated global horizontal insolation is monthly. The results show that the developed NN model has promising performance and considering the calibration process for Angstrom's model it can be used as an alternative. The NN model uses climatic data to estimate the solar insolation which makes it more flexible in terms of being applicable for different regions. (C) 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Subject Keywords

Iran, Artificial neural network, Angstrom's model, Global insolation, Solar resources

URI

https://hdl.handle.net/11511/66311

DOI

https://doi.org/10.1002/pssc.201510105

Collections

Engineering, Conference / Seminar

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Citation Formats

E. Jahani, S. M. S. Sadati, and M. Yousefzadeh, “Assessment of solar data estimation models for four cities in Iran,” 2015, vol. 12, p. 1272, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66311.