Data collected from distributed photovoltaic systems via the Internet of Things (IoT) exhibit randomness and continuous data gaps, rendering traditional data imputation methods prone to low accuracy. This paper proposes a “data-physical” driven photovoltaic output data completion model based on xLSTM-Hankel tensor CP decomposition. The model is designed with a dual branch structure. Branch 1 employs xLSTM to fully extract the global temporal features of PV data, while Branch 2 uses the Hankel tensor CP decomposition method, which accounts for the intermittent nature of PV output, to enhance the model’s generalization capability. Finally, data repair experiments were conducted using actual PV data collected from a northern province in China. The experimental results show that the filling algorithm in this paper has an RSE that is 2.08% higher than that of the GAN algorithm and an MAE that is 4.06% lower when faced with long-term data missing.
Fu et al. (Sat,) studied this question.