TransPVP: A Transformer-Based Method for Ultra-Short-Term Photovoltaic Power Forecasting

Jinfeng Wang (), Wenshan Hu, Lingfeng Xuan, Feiwu He, Chaojie Zhong and Guowei Guo
Additional contact information
Jinfeng Wang: Electric Power Science Research Institute, Guangdong Power Grid Limited Liability Company, Guangzhou 510062, China
Wenshan Hu: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Lingfeng Xuan: Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Guangzhou 513000, China
Feiwu He: Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Guangzhou 513000, China
Chaojie Zhong: Qingyuan Yingde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Guangzhou 513000, China
Guowei Guo: Foshan Shunde Power Supply Bureau, Guangdong Power Grid Limited Liability Company, Guangzhou 528300, China

Energies, 2024, vol. 17, issue 17, 1-19

Abstract: The increasing adoption of renewable energy, particularly photovoltaic (PV) power, has highlighted the importance of accurate PV power forecasting. Despite advances driven by deep learning (DL), significant challenges remain, particularly in capturing the long-term dependencies essential for accurate forecasting. This study presents TransPVP, a novel transformer-based methodology that addresses these challenges and advances PV power forecasting. TransPVP employs a deep fusion technique alongside a multi-task joint learning framework, effectively integrating heterogeneous data sources and capturing long-term dependencies. This innovative approach enhances the model’s ability to detect patterns of PV power variation, surpassing the capabilities of traditional models. The effectiveness of TransPVP was rigorously evaluated using real data from a PV power plant. Experimental results showed that TransPVP significantly outperformed established baseline models on key performance metrics including RMSE, R 2 , and CC, underscoring its accuracy, predictive power, and reliability in practical forecasting scenarios.

Keywords: power forecasting; photovoltaic power; renewable energy; deep learning; information fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (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/1996-1073/17/17/4426/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/17/4426/ (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:jeners:v:17:y:2024:i:17:p:4426-:d:1470752

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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