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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Photovoltaic power prediction based on finite scalar quantization and staged cross-modal attention fusion

Zhang Haiqing1,2
Liu Jialing1,2
Tang Dan1
Xiang Xiaoming3
Yang Dong4
Guo Benjun1,2
1. College of Software Engineering, Chengdu University of Information Engineering, Chengdu Sichuan 610225, China
2. Sichuan Provincial Engineering Research Center for Intelligent Tolerance Design & Measurement, Chengdu Sichuan 610255, China
3. Sichuan Meteorological Observation & Data Center, Chengdu 610072, China
4. Sichuan Meteorological Service Center, Chengdu 610072, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0159

Abstract

To address the limitations of multi-modal fusion in photovoltaic power prediction—specifically the challenges posed by modality heterogeneity in data representation and insufficient cross-modal correlation modeling—this study proposes a multi-modal meteorological data fusion model (MMDF) . The model first applies Finite Scalar Quantization (FSQ) to achieve unified representation of heterogeneous data sources, effectively overcoming the bottleneck of cross-modal information alignment and reducing computational complexity during fusion. Then, a hybrid encoding feature extraction module is designed by integrating the global spatial modeling capability of the Vision Transformer with the temporal dynamics capture of the GRU-Linear architecture, which significantly enhances the discriminability of multi-modal features. Furthermore, a staged cross-modal fusion strategy is constructed based on a gated dynamic fusion mechanism, and the Cross Transformer is employed to deeply integrate time-series and cloud image features, thereby capturing complex inter-modal relationships. Experimental results show that, compared with the FusionSF algorithm, the proposed MMDF model improves MAE, RMSE, and R² by 5.69%, 12.44%, and 4.52%, respectively, on a multi-modal solar power dataset, providing both a theoretical breakthrough and a new paradigm for engineering applications in photovoltaic power prediction under complex weather conditions.

Foundation Support

四川省科学技术厅项目(2025YFHZ0219):基于量子动力学优化算法的复杂装备几何精度分析与优化方法研究
青海省气象局"揭榜挂帅"科技项目:基于机器学习的强对流智能监测识别技术研究("赛马"项目编号:QXGS2023-01)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0159
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-20] Accepted Paper

Cite This Article

张海清, 刘珈伶, 唐聃, 等. 基于有限标量量化与分阶段跨模态注意力融合的光伏功率预测 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0159. (Zhang Haiqing, Liu Jialing, Tang Dan, et al. Photovoltaic power prediction based on finite scalar quantization and staged cross-modal attention fusion [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0159. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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