Analysis of correlation of climate factors affecting solar power generation

  • Abstract
  • Keywords
  • References
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  • Abstract

    Background/Objectives: In designing the solar power generation, feasibility review and power generation volume prediction during guarantee phase after the completion are very important.

    Methods/Statistical analysis: The study compares the actual power generation volume obtained from solar power generation monitoring system and estimated volume calculated using overseas meteorological data from Meteonorm 7.1 and NASA-SSE and Korean data from the Korea Meteorological Administration, in order to understand their accuracy. The calculation using KMA data, with the highest prediction value, was used to analyze the correlation among solar radiation, temperature, and solar power generation volume.

    Findings: Previous solar power generation volume prediction was conducted only with solar radiation value, which caused errors between the actual and predicted solar power generation volume. The study found that the power generation volume and solar radiation have a high positive correlation coefficient of 0.8131 for Songam Power Plant. For correlation between power generation volume and temperature, the coefficient for Songam was 0.2843 and 0.4616 for Jipyeong Power Plant, showing lower influence than that of solar radiation. In sum, solar radiation influences the solar power generation volume more than temperature, but the current study indicates that both solar radiation and temperature must be considered for an accurate prediction of solar power generation volume.

    Improvements/Applications: Research to develop solar power generation volume prediction algorithm that takes into account both solar radiation and temperature must be conducted to expand the application of solar power generation system with more accurate estimation of power generation volume.



  • Keywords

    Photovoltaic Power Generation; Low Carbon Energy; Photovoltaics; PV System; Photovoltaic Power Generation Prediction

  • References

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Article ID: 14185
DOI: 10.14419/ijet.v7i2.33.14185

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