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Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant

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  • Imtiaz Ashraf
  • A. Chandra

Abstract

This paper presents an artificial neural network (ANN) approach for forecasting the performance of electric energy generated output from a working 25-kWp grid connected solar PV system and a 100-kWp grid connected PV system installed at Minicoy Island of Union Territory of Lakshadweep Islands. The ANN interpolates among the solar PV generation output and relevant parameters such as solar radiation, module temperature and clearness index. In this study, three ANN models are implemented and validated with reasonable accuracy on real electric energy generation output data. The first model is univariate based on solar radiation and the output values. The second model is a multivariate model based on module temperature along with solar radiation. The third model is also a multivariate model based on module temperature, solar radiation and clearness index. A forecasting performance measure such as percentage root mean square error has been presented for each model. The second model, which gives the most accurate results, has been used in forecasting the generation output for another PV system with similar accuracy.

Suggested Citation

  • Imtiaz Ashraf & A. Chandra, 2004. "Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 21(1/2), pages 119-130.
    • Handle: RePEc:ids:ijgeni:v:21:y:2004:i:1/2:p:119-130
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    Citations

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    Cited by:

    1. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    2. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
    3. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.
    4. Olivencia Polo, Fernando A. & Ferrero Bermejo, Jesús & Gómez Fernández, Juan F. & Crespo Márquez, Adolfo, 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, Elsevier, vol. 81(C), pages 227-238.
    5. Su, Yan & Chan, Lai-Cheong & Shu, Lianjie & Tsui, Kwok-Leung, 2012. "Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems," Applied Energy, Elsevier, vol. 93(C), pages 319-326.
    6. Pérez-Alonso, J. & Pérez-García, M. & Pasamontes-Romera, M. & Callejón-Ferre, A.J., 2012. "Performance analysis and neural modelling of a greenhouse integrated photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4675-4685.
    7. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.

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