Spatial‐temporal learning structure for short‐term load forecasting

Abstract

Abstract In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers’ consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data.

Keywords

• bidirectional gated recurrent unit
• deep graph network
• encoder–decoder
• Gabor convolutional neural network
• meteorological data
• short‐term load forecasting