Nonparametric End-to-End Probabilistic Forecasting of Distributed
  Generation Outputs Considering Missing Data Imputation
Minghui Chen Zichao Meng Yanping Liu Longbo Luo Ye Guo Kang Wang
In this paper, we introduce a nonparametric end-to-end method for
probabilistic forecasting of distributed renewable generation outputs while
including missing data imputation. Firstly, we employ a nonparametric
probabilistic forecast model utilizing the long short-term memory (LSTM)
network to model the probability distributions of distributed renewable
generations' outputs. Secondly, we design an end-to-end training process that
includes missing data imputation through iterative imputation and iterative
loss-based training procedures. This two-step modeling approach effectively
combines the strengths of the nonparametric method with the end-to-end
approach. Consequently, our approach demonstrates exceptional capabilities in
probabilistic forecasting for the outputs of distributed renewable generations
while effectively handling missing values. Simulation results confirm the
superior performance of our approach compared to existing alternatives.