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Korea Multimedia Society (한국멀티미디어학회)
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Solar Power Generation Prediction Algorithm Using the Generalized Additive Model

일반화 가법모형을 이용한 태양광 발전량 예측 알고리즘

  • Received : 2022.09.20
  • Accepted : 2022.11.03
  • Published : 2022.11.30
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Abstract

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

Keywords

Acknowledgement

This work was supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government(MOTIE)(20203040010420, ICT safety management technology development and business model demonstration through performance enhancement of small-capacity obsolescene photovoltaic(heating) system facilities).

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