Imputation and forecasting for Multi-Output Gaussian Process in Smart Grid

Data imputation and prediction is a key component of intelligent upgrading of power systems. Data obtained from the real world may have varying degrees of missing data. These missing components have a significant impact on the outcome of the prediction model. In addition, the single-objective method lacks the ability to establish correlation models between multiple datasets, which can not improve the accuracy of data imputation and forecasting. To handle multi-output imputation and forecasting problems, this paper a novel kernel-based multi-output Gaussian process (MOGP) model to achieve data imputation and prediction simultaneously.