Multioutput Framework for Time-Series Forecasting in Smart Grid Meets Data Scarcity

Sensor technology has become increasingly prevalent in various domains of human life. However, the collected data often contains missing values to varying degrees. Moreover, obtaining sufficient historical data, particularly for smart grid data forecasting in isolated networks, is often challenging. These data deficiencies can negatively impact the forecasting accuracy of deep-learning models, consequently affecting the operational performance of microgrids. To address these challenges, this article introduces a multioutput learning framework based on the multioutput Gaussian process (MOGP) model. This framework aims to achieve data imputation and prediction by leveraging the correlation between tasks simultaneously, even with limited data availability. To assess the effectiveness of the proposed method, experiments are conducted on three types of data. The empirical results demonstrate that the MOGP model outperforms two alternative techniques in terms of imputation and forecasting performance across all cases. Furthermore, to mitigate computational complexity, a novel kernel approximation method based on random Fourier features is proposed. The experimental results validate the effectiveness of this approach, as it significantly reduces computational complexity while maintaining satisfactory performance levels.