Online SARIMA applied for short-term electricity load forecasting

Abstract

Short-term Load Forecasting (STLF) plays a crucial role in balancing the supply and demand of load dispatching operations and ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast the hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.