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Research on the distributed photovoltaic power prediction method based on CNN–LSTM

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  • Hua Ye
  • Tao Xu

Abstract

The proposed model is an enhanced prediction model that utilizes a convolutional neural network (CNN) and a long- and short-term memory network (LSTM). The model first preprocesses the raw data to determine the key input features of the prediction model. Subsequently, a hybrid model comprising a CNN and a LSTM network was developed. The CNN is responsible for capturing spatial correlations between different geographical locations, while the LSTM focuses on identifying long-term dependencies in the photovoltaic time series. The experimental results demonstrate that the CNN–LSTM-based prediction model attains high prediction accuracy, thereby substantiating the efficacy and preeminence of this methodology.

Suggested Citation

  • Hua Ye & Tao Xu, 2026. "Research on the distributed photovoltaic power prediction method based on CNN–LSTM," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 21, pages 1-8.
    • Handle: RePEc:oup:ijlctc:v:21:y:2026:i::p:1-8.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf131
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