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.
photovoltaic power prediction/Neural Hierarchical Interpolation for Time Series(N-HiTS)/Integrated Correlation Measurement(ICM)/K-Nearest Neighbors(KNN)/linear interpolation
维普
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