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A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast

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  • Kushwaha, Vishal
  • Pindoriya, Naran M.

Abstract

A very short-term solar PV power generation forecast can be extremely helpful for real-time balancing operation in an electricity market which in turn will profit both energy suppliers as well as customers. However, the intermittency of solar PV power introduces inaccuracies in its forecast. To address this challenge, the research paper has studied the effect of wavelet decomposition of solar PV power time series on its forecast. A novel and time adaptive, Seasonal Autoregressive Integrated Moving Average (SARIMA)-Random Vector Functional Link (RVFL) neural network hybrid model assisted by Maximum Overlap Discrete Wavelet Transform (MODWT) has been proposed. The solar PV power generation data obtained from roof-top solar PV plants installed at IIT Gandhinagar is used to develop and validate the forecast models. Various numerical forecast accuracy measures have been calculated which show an improvement in accuracy and adaptability of proposed forecast model over constituent models.

Suggested Citation

  • Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    • Handle: RePEc:eee:renene:v:140:y:2019:i:c:p:124-139
    DOI: 10.1016/j.renene.2019.03.020
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    References listed on IDEAS

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