App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

Multivariate photovoltaic power forecasting based on time-frequency decomposition and channel interaction awareness

Li Zheng1,2
Wu Wenli1
Qin Jinlei1,3
Wu Heng1
1. Dept. of Computer, North China Electric Power University, Baoding Hebei 071003, China
2. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, North China Electric Power University, Baoding Hebei 071003, China
3. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding Hebei 071003, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0293

Abstract

To improve the accuracy of multivariate photovoltaic (PV) power prediction, this study proposes a multivariate PV power prediction model based on time-frequency decomposition and channel interaction awareness. Aiming at the issue that existing time-series decomposition methods relying on basic moving average kernels struggle to handle the nonlinear structures and complex trends of PV power data, a dual decomposition mechanism integrating the time domain and frequency domain is designed to enhance the modeling capability for non-stationary sequences. To overcome the limitation that channel-independent methods ignore the potential correlations among multiple variables, a channel interaction-aware method is constructed. In addition, addressing the shortcomings of traditional PV power prediction—such as neglecting the differences in time-step weights, changes in long-short-term correlations, and time-dependent features—a joint loss function is introduced. This function combines mean squared error (MSE) , signal attenuation loss, and first-order difference loss using an adaptive weight adjustment scheme. Experiments on four actual PV datasets show that, compared with the optimal benchmark model, the proposed model reduces the MSE and mean absolute error (MAE) by an average of 5.59% and 5.01%, respectively, with the maximum reductions reaching 7.40% and 8.80%. The results demonstrate that the model significantly improves prediction accuracy and mitigates the cumulative effect of errors in the time dimension.

Foundation Support

河北省自然科学基金资助项目(F2014502081)
中央高校基本科研业务费专项基金资助项目(2020MS120)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0293
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-16] Accepted Paper

Cite This Article

李整, 武文丽, 秦金磊, 等. 融合时频分解与通道交互感知的多变量光伏功率预测 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0293. (Li Zheng, Wu Wenli, Qin Jinlei, et al. Multivariate photovoltaic power forecasting based on time-frequency decomposition and channel interaction awareness [J]. Application Research of Computers, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0293. )

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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