Robust photovoltaic forecasting under severe data missingness via multi-domain collaboration and covariate interaction

High-quality photovoltaic (PV) power forecasting is essential for efficient energy management and reliable grid integration, yet real-world data are often plagued by extensive missingness in both target and auxiliary variables. To address this challenge, we propose MDCTL-MCI, a missingness-aware forecasting framework that jointly leverages signal decomposition, multi-scale covariate interaction, and multi-domain collaborative transfer learning. First, multivariate singular spectrum analysis (MSSA) denoises and reconstructs incomplete time series, enhancing underlying temporal structures without explicit imputation. Next, a lightweight multiscale covariate interaction (MCI) module models interactions among reconstructed PV power, global horizontal irradiance, direct normal irradiance, and total solar irradiance at varying temporal resolutions, capturing both local fluctuations and global trends. Finally, a multi-source domain collaborative transfer learning strategy aggregates knowledge from multiple PV sites to form a global model, which is then fine-tuned on a small set of high-quality, MSSA-processed samples at each site. By freezing all but the output layer during fine-tuning, MDCTL-MCI adapts efficiently to local data heterogeneity. Extensive experiments on four Chinese PV installations reveal that, compared to baseline methods, the proposed method improves average accuracy by 10.5 % under complete data conditions and by 15.3 % under various missing data scenarios. • Evaluating whether covariate inclusion enhances model forecasting performance. • Maintaining prediction accuracy and robustness under extensive input-data missingness. • Showing through experiments that covariates improve solar irradiance forecasts. • First examining photovoltaic forecasting under data-missing conditions.