App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 10

Photovoltaic power prediction integrating deep clustering and adaptive non-stationary regulation

Zhang Haiqing1,2
Zou Yifei1,2
Yang Mengji3
Tang Xin4
Li Daiwei1,2
Guo Benjun1,2
1. College of Software Engineering, Chengdu University of Information Engineering, Chengdu 610225, China
2. Sichuan Provincial Engineering Research Center for Intelligent Tolerance Design and Measurement, Chengdu 610225, China
3. College of Computer Science, Chengdu University, Chengdu 610106, China
4. Dept. of Electronic Information and Computer Engineering, The Engineering&Technical College of Chengdu University of Technology, Leshan Sichuan 614000, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0039

Abstract

Meteorological disturbances and changes in operating states affect photovoltaic power sequences and cause complex uncertainty and non-stationarity. Existing methods remain insufficient in modeling latent operating patterns and time-varying dependencies. To address this issue, this paper proposed a photovoltaic power forecasting method that integrates deep clustering with adaptive non-stationary regulation. First, this paper designed a Time-enhanced Deep Embedded Clustering algorithm that uses sliding windows. Within local time windows, the algorithm mapped samples into a low-dimensional embedding space and introduced sine-cosine temporal phase encoding into the clustering assignment process. The algorithm generated a membership matrix that reflected latent operating states and enhanced the scenario representation capability of input features. Second, this paper constructed a Non-stationary Regulation Attention module. The module used cross-scale statistical differences to calculate the variances of multi-scale features and obtain non-stationary coefficients, which regulated the sampling offsets and Value components of deformable attention. Finally, this paper designed a two-dimensional Kalman filtering. It used the forecast value and its variation to construct a state space and recursively corrected the forecast sequence to suppress random disturbances and maintain trend continuity. Experiments on multiple public photovoltaic datasets showed that this method outperformed the second-best model by reducing MSE and MAE by 6.6% and 8.1% on average, respectively, and improving R² by 2.3%. These results verify its effectiveness in non-stationary photovoltaic power forecasting.

Foundation Support

四川省科学技术厅资助项目(2025YFHZ0219)
青海省气象局"揭榜挂帅"科技项目(QXGS2023-01)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0039
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 10

Publish History

[2026-07-08] Accepted Paper

Cite This Article

张海清, 邹亦飞, 杨孟辑, 等. 融合深度聚类与自适应非平稳调控的光伏功率预测 [J]. 计算机应用研究, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0039. (Zhang Haiqing, Zou Yifei, Yang Mengji, et al. Photovoltaic power prediction integrating deep clustering and adaptive non-stationary regulation [J]. Application Research of Computers, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0039. )

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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