Forecasting of Photovoltaic Power with ARO based AI approach

Authors

  • Srinivasa Rao Bittla ,Srimaan Yarram

Keywords:

Convolutional Neural Network; Artificial rabbit algorithm; Power systems; Lévy flight; AlexNet.)

Abstract

Sunlight is the key to renewable power that can supply the smart grid of the future with vast quantities of electricity. Unfortunately, the unpredictability and intermittent nature of solar energy resources provide challenges for the systems. Future smart grid optimization and planning are significantly hampered by solar power's inherent unpredictability. Reducing the intermittent nature of power requires a precise estimation of photovoltaic (PV) power generation.

References

de Oliveira, R.A., Ravindran, V., Rönnberg, S.K. and Bollen, M.H., 2021. Deep learning method with manual post-processing for identification of spectral patterns of waveform distortion in PV installations. IEEE Transactions on Smart Grid, 12(6), pp.5444-5456.

Manno, D., Cipriani, G., Ciulla, G., Di Dio, V., Guarino, S. and Brano, V.L., 2021. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management, 241, p.114315.

Zhang, C., Li, Z., Jiang, H., Luo, Y. and Xu, S., 2021. Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China. Applied Energy, 283, p.116329.

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Published

2024-10-12

How to Cite

Srinivasa Rao Bittla ,Srimaan Yarram. (2024). Forecasting of Photovoltaic Power with ARO based AI approach . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 1690–1700. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1807

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