Day-Ahead Photovoltaic Power Forecasting Based on SN-Transformer-BiMixer

Xiaohong Huang, Xiuzhen Ding, Yating Han, Qi Sima, Xiaokang Li and Yukun Bao ()
Additional contact information
Xiaohong Huang: Intelligence & Integrity Energy Technology Co., Ltd., Wuhan 430010, China
Xiuzhen Ding: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
Yating Han: Intelligence & Integrity Energy Technology Co., Ltd., Wuhan 430010, China
Qi Sima: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaokang Li: Intelligence & Integrity Energy Technology Co., Ltd., Wuhan 430010, China
Yukun Bao: Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 16, 1-27

Abstract: Accurate forecasting of photovoltaic (PV) power is crucial for ensuring the safe and stable operation of power systems. However, the practical implementation of forecasting systems often faces challenges due to missing real-time historical power data, typically caused by sensor malfunctions or communication failures, which substantially hamper the performance of existing data-driven time-series forecasting techniques. To address these limitations, this study proposes a novel day-ahead PV forecasting approach based on similar-day analysis, i.e., SN-Transformer-BiMixer. Specifically, a Siamese network (SN) is employed to identify patterns analogous to the target day within a historical power dataset accumulated over an extended period, considering its superior ability to extract discriminative features and quantify similarities. By identifying similar historical days from multiple time scales using SN, a baseline generation pattern for the target day is established to allow forecasting without relying on real-time measurement data. Subsequently, a transformer model is used to refine these similar temporal curves, yielding improved multi-scale forecasting outputs. Finally, a bidirectional mixer (BiMixer) module is designed to synthesize similar curves across multiple scales, thereby providing more accurate forecast results. Experimental results demonstrate the superiority of the proposed model over existing approaches. Compared to Informer, SN-Transformer-BiMixer achieves an 11.32% reduction in root mean square error (RMSE). Moreover, the model exhibits strong robustness to missing data, outperforming the vanilla Transformer by 8.99% in RMSE.

Keywords: photovoltaic forecasting; deep learning; Siamese network; multi-scale; bidirectional mixer (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/16/4406/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/16/4406/ (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:18:y:2025:i:16:p:4406-:d:1727372

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 ().