Abstract: Aiming at the problem that the prediction accuracy of photovoltaic power is affected by data quality and exogenous variables,the ultra-short-term multi-step photovoltaic power prediction approach is proposed integrating exogenous variable analysis,data quality control and Neural Hierarchical Interpolation for Time Series（N-HiTS）model. Firstly,the proposed Integrated Correlation Measurement（ICM）for screening exogenous variables is proposed,and the K-Nearest Neighbors（KNN）algorithm and linear interpolation strategy are used to deal with the problem of missing data. Secondly,a long-term prediction model based on the N-HiTS model is established to improve the model’s proficiency in processing long-term series time data through multi-scale signal sampling and hierarchical interpolation. Finally,a comparative analysis between the proposed method and the traditional photovoltaic forecasting techniques is conducted through a numerical example to verify the prediction accuracy of the proposed method.