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Clearness index predicting using an integrated artificial neural network (ANN) approach

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  • Kheradmanda, Saeid
  • Nematollahi, Omid
  • Ayoobia, Ahmad Reza

Abstract

Accurate insolation data for many cities and locations is not available. Therefore estimation of such data for solar applications is inevitable. Clearness index KT¯ is one of the parameters which represent the atmosphere characteristics and solar energy potential at a location. This paper presents an Integrated Artificial Neural Network (ANN) approach for optimum forecasting of Clearness index by considering environmental and meteorological factors. The ANN train and test data with multi-layer perceptron (MLP) approach which is popular and applicable network for such engineering investigations is used in this study. The proposed approach is particularly useful for locations with no available measurement equipment. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 30 years (1975–2005) in 19 nominal cities in Iran. The acquired results of the model have shown high accuracy with a mean absolute percentage error (MAPE) about 4.338%. Furthermore, a detailed analysis is performed on the various combinations of input parameters. Finally, using these results, geographic information system (GIS) map is produced and presented. This map is very good indicative of climate and solar potential of different locations based on ANN analysis.

Suggested Citation

  • Kheradmanda, Saeid & Nematollahi, Omid & Ayoobia, Ahmad Reza, 2016. "Clearness index predicting using an integrated artificial neural network (ANN) approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1357-1365.
    • Handle: RePEc:eee:rensus:v:58:y:2016:i:c:p:1357-1365
    DOI: 10.1016/j.rser.2015.12.240
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    Cited by:

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    2. Ibrahim, Thamir k. & Mohammed, Mohammed Kamil & Awad, Omar I. & Rahman, M.M. & Najafi, G. & Basrawi, Firdaus & Abd Alla, Ahmed N. & Mamat, Rizalman, 2017. "The optimum performance of the combined cycle power plant: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 459-474.
    3. Firozjaei, Mohammad Karimi & Nematollahi, Omid & Mijani, Naeim & Shorabeh, Saman Nadizadeh & Firozjaei, Hamzeh Karimi & Toomanian, Ara, 2019. "An integrated GIS-based Ordered Weighted Averaging analysis for solar energy evaluation in Iran: Current conditions and future planning," Renewable Energy, Elsevier, vol. 136(C), pages 1130-1146.
    4. Martins, Guilherme Santos & Giesbrecht, Mateus, 2021. "Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 787-805.
    5. Wang, H. & Sánchez-Molina, J.A. & Li, M. & Berenguel, M. & Yang, X.T. & Bienvenido, J.F., 2017. "Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms, back-propagation neural networks and nonlinear autoregressive exogenous ," Agricultural Water Management, Elsevier, vol. 183(C), pages 107-115.
    6. Mehreen Gul & Yash Kotak & Tariq Muneer & Stoyanka Ivanova, 2018. "Enhancement of Albedo for Solar Energy Gain with Particular Emphasis on Overcast Skies," Energies, MDPI, vol. 11(11), pages 1-17, October.

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