Robust autoregressive bidirectional gated recurrent units model for short-term power forecasting

Abstract

Accurate short-term power forecasting (STPF) provides reliable support for the stable operation of power systems. However, due to the randomness of consumer behavior and energy properties, outliers inevitably exist in power series. Considering its negative influence, effectively extracting features from the power series with outliers has become a significant challenge in STPF. This paper develops a robust hybrid model to handle this issue. The proposed model utilizes the robust regression technique to handle outliers. An adaptive rescaled Huber loss is developed to approximate the complex distribution of the actual power series. Moreover, the proposed model applies autoregressive and bidirectional gated recurrent units to extract linear and nonlinear features of power series, respectively. Meanwhile, the attention mechanism extracts the temporal feature through the attention representation, which considers the correlations between different moments. The proposed model obtains the optimal coefficients of determination between predictions and observations on the wind power series as 0.9629 and power load series as 0.978, which indicates that the proposed model performs competitive robustness and generalization on the daily operation of renewable energy systems.