What question did this study set out to answer?

The aim is to develop an advanced prediction model for photovoltaic power using CNN and LSTM networks.

February 19, 2026International Journal of Low-Carbon Technologies0 citationsOpen Access

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

Key Points

The aim is to develop an advanced prediction model for photovoltaic power using CNN and LSTM networks.
Developed a hybrid model combining CNN and LSTM networks.
Preprocessed raw data to identify key input features.
Used CNN to capture spatial correlations between geographical locations.
Employed LSTM to detect long-term dependencies in time series data.
The CNN-LSTM model achieved high prediction accuracy.
Experimental results confirmed the efficacy of the proposed methodology.

Abstract

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.

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