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Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements

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  • Wang, Meng
  • Peng, Jinqing
  • Luo, Yimo
  • Shen, Zhicheng
  • Yang, Hongxing

Abstract

This paper tested the energy outputs of different types of PV modules and evaluated the accuracies of different simplistic PV module power prediction models. A test rig was developed in Hong Kong to assess the PV power outputs of ten PV modules. The solar radiation, ambient temperature and power generation are recorded. The evaluated models include simple efficiency model, temperature correction model and one-diode model. The results show that the mono-Si PV module is the highest in terms of the annual energy outputs per unit area, and a-Si PV modules is the lowest. The simple efficiency model overestimates the power output of all types of PV modules for more than 10% except for a CdTe PV module. The one-diode model demonstrated the highest accuracy for mono-Si and poly-Si PV modules. The accuracies of the evaluated models are low for thin-film PV technologies. Although the mean bias error of the one-diode model is larger than 10% for the thin-film PV modules (expect for one CdTe PV module), the one-diode model has the highest accuracy among the three models. Further studies should be conducted to investigate the energy performance of thin-film PV modules and then improve their prediction accuracy.

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  • Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004114
    DOI: 10.1016/j.energy.2021.120162
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    Cited by:

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    6. Dai, Yeming & Wang, Yanxin & Leng, Mingming & Yang, Xinyu & Zhou, Qiong, 2022. "LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method," Energy, Elsevier, vol. 256(C).
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