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Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data

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  • Nourani, Vahid
  • Sharghi, Elnaz
  • Behfar, Nazanin
  • Zhang, Yongqiang

Abstract

In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively.

Suggested Citation

  • Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:appene:v:315:y:2022:i:c:s0306261922004627
    DOI: 10.1016/j.apenergy.2022.119069
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