Work

High precision prediction of time-varying photovoltaic power based on dynamic adjacency matrix and temporal spectral graph convolution network

Year: 2026

Type: article

Abstract: Accurate photovoltaic (PV) power generation forecasts are crucial for renewable power scheduling and integration. However, the inherent intermittency and volatility renders the PV data time-variant, posing significant challenges for accurate prediction. To address this issue, we propose a novel hybrid forecasting model named adaptive temporal spectral graph convolutional network (ATS-GCN) to combine multi-domain information and dynamically extract truly relevant adjacency matrices, proving high precision PV power prediction under evolving and hyperdynamic scenarios. The ATS-GCN framework combines a continuous wavelet transform module for spectral feature extraction, an adaptive feature extractor based on curvilinear Mahalanobis distance for time-varying correlations calculating among PV data, a graph convolutional network and a gated recurrent unit for spatial–temporal features capturing. Furthermore, an optimized dropout strategy is incorporated to prevent overfitting. By fusing nonlinear features from multiple domains and dynamically capturing the time-varying correlations, ATS-GCN not only improves prediction accuracy but also enhances the cross-seasonal stability and interpretability of PV prediction. The proposed model is compared with baseline models on seven PV datasets with 3-step and 24-step forecasting horizon, achieving the smallest average error interquartile range span of 1.96 kW and the smallest average error median of 0.31 kW, proving the superiority of ATS-GCN. These results demonstrate the model’s strong predictive capability for practical PV power generation, rendering it a valuable tool for advancing intelligent energy management and supporting renewable energy integration into smart grids. • A novel ATS-GCN model for short-term PV power prediction is proposed. • Adaptive feature extractor based on curvilinear Mahalanobis distance captures nonlinear and time-varying PV data correlation. • Continuous wavelet transform extracting high and low spectral components of all PV data improves prediction accuracy. • An optimized dropout strategy designed for GCN-GRU framework improves prediction robustness.

Source: Energy Conversion and Management X

DOI: 10.1016/j.ecmx.2026.101676

Authors: Honglei Guo, Zhenchan Su, Zhiwei Wang, Guiren Zhan, Changjian Liu, Ling Bu

Institutions: China University of Geosciences (Beijing), Peking University

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,170