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RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

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This article was retracted on 09 March 2020

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Abstract

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively.

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Change history

  • 09 March 2020

    The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2, 3]) and authorship manipulation.

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Authors and Affiliations

  1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Abdullah Gani & Shahaboddin Shamshirband

  2. Faculty of Mechanical Engineering, University of Kashan, Kashan, Iran

    Kasra Mohammadi & Hossein Khorasanizadeh

  3. Department of Water Engineering, Soil and Water College, University of Zabol, Zabol, IR, Iran

    Jamshid Piri

  4. Advanced Informatics School, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Zuraini Ismail & Mazdak Zamani

  5. Department of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Amir Seyed Danesh

Authors
  1. Abdullah Gani
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  2. Kasra Mohammadi
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  3. Shahaboddin Shamshirband
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  4. Hossein Khorasanizadeh
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  5. Amir Seyed Danesh
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  6. Jamshid Piri
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  7. Zuraini Ismail
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  8. Mazdak Zamani
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Corresponding author

Correspondence to Kasra Mohammadi.

Additional information

The Editor-in-Chief has retracted this article because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with two articles; see retraction note for details) and authorship manipulation. Shahaboddin Shamshirband disagrees with this retraction. Authors Abdullah Gani Kasra Mohammadi, Hossein Khorasanizadeh, Amir Seyed Danesh, Jamshid Piri, Zuraini Ismail, Mazdak Zamani have not responded to correspondence about this retraction.

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Gani, A., Mohammadi, K., Shamshirband, S. et al. RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model. Theor Appl Climatol 125, 679–689 (2016). https://doi.org/10.1007/s00704-015-1533-8

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  • DOI: https://doi.org/10.1007/s00704-015-1533-8

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