Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer

Keyong Hu (), Zheyi Fu, Chunyuan Lang, Wenjuan Li, Qin Tao and Ben Wang ()
Additional contact information
Keyong Hu: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Zheyi Fu: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Chunyuan Lang: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Wenjuan Li: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Qin Tao: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Ben Wang: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

Sustainability, 2024, vol. 16, issue 14, 1-18

Abstract: The intermittent nature of solar energy poses significant challenges to the integration of photovoltaic (PV) power generation into the electrical grid. Consequently, the precise forecasting of PV power output becomes essential for efficient real-time power system dispatch. To meet this demand, this paper proposes a deep learning model, the CA-Transformer, specifically designed for PV power output prediction. To overcome the shortcomings of traditional correlation coefficient methods in dealing with nonlinear relationships, this study utilizes the Copula function. This approach allows for a more flexible and accurate determination of correlations within time series data, enabling the selection of features that exhibit a high degree of correlation with PV power output. Given the unique data characteristics of PV power output, the proposed model employs a 1D-CNN model to identify local patterns and trends within the time series data. Simultaneously, it implements a cosine similarity attention mechanism to detect long-range dependencies within the time series. It then leverages a parallel structure of a 1D-CNN and a cosine similarity attention mechanism to capture patterns across varying time scales and integrate them. In order to show the effectiveness of the model proposed in this study, its prediction results were compared with those of other models (LSTM and Transformer). The experimental results demonstrate that our model outperforms in terms of PV power output prediction, thereby offering a robust tool for the intelligent management of PV power generation.

Keywords: photovoltaic power forecast; copula function; convolutional neural network; transformer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/14/5940/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/14/5940/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:14:p:5940-:d:1433660

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().