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An Evaluation Method of the Photovoltaic Power Prediction Quality

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  • Mao Yang
  • Xin Huang

Abstract

Photovoltaic (PV) output power has regularity, volatility, and randomness. First of all, this paper carried on a metrological analysis to PV system data. Then, this paper analyzed the relationship between PV historical data, PV power forecasting model, and forecast error. By spectrum analysis of PV power, the PV power is decomposed into periodic components, low frequency residual components, and high frequency residual components. Making a specific analysis of these three components determines the minimum modeling error value, which reflects the unpredictable part of the PV power. Determining the minimum modeling error for PV forecasting not only objectively evaluates the quality of the PV forecasting model but also can determine the prediction accuracy standard according to different PV power generation targets. The examples given in this paper illustrate the effectiveness of the method.

Suggested Citation

  • Mao Yang & Xin Huang, 2018. "An Evaluation Method of the Photovoltaic Power Prediction Quality," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, March.
    • Handle: RePEc:hin:jnlmpe:9049215
    DOI: 10.1155/2018/9049215
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    Cited by:

    1. Ali Kamil Gumar & Funda Demir, 2022. "Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks," Energies, MDPI, vol. 15(22), pages 1-15, November.

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