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Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement

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  • Xu, Shaozhen
  • Liu, Jun
  • Huang, Xiaoqiao
  • Li, Chengli
  • Chen, Zaiqing
  • Tai, Yonghang

Abstract

In recent years, the government has attached great importance to promoting innovation in new energy technologies, particularly in the area of solar power generation. Photovoltaic (PV) power generation has become an essential factor in the promotion of renewable energy due to its inherent safety, environmental sustainability and abundant solar energy resources. The efficiency and stability of PV generation systems are closely related to the accurate prediction of real-time global horizontal irradiance (GHI). However, some existing research methods have made some progress in hourly resolution, but the multi-step prediction accuracy and model generalization ability in high time resolution are still unsatisfactory. Therefore, this paper proposes a novel minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement scheme. In this scheme, the advantages of both LSTM and InformerStack models are combined to further enhance the global and local features, which can better capture the short-term time series patterns and long-term dependencies and improve the accuracy of the time series. The experimental validation results show that the performance of this prediction scheme is improved by 11.2%, and 26.0% for nRMSE and R2, respectively, and the 24-step prediction can be improved up to 26.1% vs. the persistent model. Meanwhile, the prediction scheme of this paper is also expected to provide a reference in future multi-step time series accurate prediction with higher resolution.

Suggested Citation

  • Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124002003
    DOI: 10.1016/j.renene.2024.120135
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    References listed on IDEAS

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