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

Hua Ye and Tao Xu

International Journal of Low-Carbon Technologies, 2026, vol. 21, 1-8

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.

Keywords: photovoltaic power prediction; clustering algorithm; long- and short-term memory network; sparrow search algorithm (search for similar items in EconPapers)
Date: 2026
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