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Estimation of solar radiation by joint application of phase space reconstruction and a hybrid neural network model

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Abstract 

Estimation of solar radiation can play a key role in environmental management as well as other fields of energy, agriculture, and hydrological and ecological modeling. In some areas, there are not enough solar radiation data due to a lack of pyranometer or its breakdown from time to time. Hence, having an estimation set at hand to estimate solar radiation based on other climatic variables is crucial. In order to develop an estimation tool, two models are applied simultaneously as a new hybrid model for estimation of monthly global solar radiation for three regions in Iran as case studies of this research work: (1): an artificial neural network (ANN) optimized with Harris hawk’s optimization (HHO) algorithm (ANNHHO) and (2) phase space reconstruction (PSR) integrated with the ANNHHO hybrid model (PSR-ANNHHO). Monthly meteorological data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), sunshine hours (SH), wind speed (U2), and relative humidity (RH) of 37 years (1985–2018) from three regions in Iran with different climate types were employed for training and testing the developed models. To select appropriate input variables for the models, a relief algorithm was applied. The performance of the new hybrid models is compared with the stand-alone ANN model. The obtained results revealed that although all the intelligent models perform satisfactorily, the hybrid PSR-ANNHHO model outperforms the hybrid ANNHHO and stand-alone ANN models in all regions. The hybrid ANN-HHO model follows the PSR-ANNHHO model as the second most accurate model.

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Data availability

The datasets used in this study, were compiled and supplied by the Meteorology Organization in Iran. They are available from the corresponding author on reasonable request.

Code availability

The code generated in this study is available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the editors and the unknown reviewers for improving the quality of the paper.

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Authors and Affiliations

  1. Department of Water Engineering, Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Mahsa H. Kashani

  2. Department of Geomatics Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpasa University, Tokat, Turkey

    Samed Inyurt

  3. Faculty of Water Sciences Engineering, University of Shahid Chamran Ahvaz, Ahvaz, Iran

    Mohammad Reza Golabi

  4. Institute of the Environment, University of Tabriz, Tabriz, Iran

    Mohammad AmirRahmani

  5. Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC

    Shahab S. Band

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  1. Mahsa H. Kashani
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  4. Mohammad AmirRahmani
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Contributions

Conceptualization, Writing-review and editing: Mahsa H. Kashani.

Formal analysis and investigation: Samed Inyurt.

Data collection and data analysis, Methodology: Mohammad Reza Golabi.

Methodology: Mohammad AmirRahmani.

Methodology and Revising manuscript: Shahab S. Band.

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mahsa H. Kashani.

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Kashani, M.H., Inyurt, S., Golabi, M.R. et al. Estimation of solar radiation by joint application of phase space reconstruction and a hybrid neural network model. Theor Appl Climatol 147, 1725–1742 (2022). https://doi.org/10.1007/s00704-021-03913-5

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