Work

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

Year: 2025

Type: article

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.

Source: International Journal of Low-Carbon Technologies

DOI: 10.1093/ijlct/ctaf131

Authors: Hua Ye, Tao Xu

Institutions: Tianjin University, Yunnan Power Grid Co., Ltd. (China)

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,886