A multitask graph convolutional network with attention-based seasonal-trend decomposition for short-term load forecasting

Accurate short-term load forecasting is important for the safe and effective functioning of modern power systems. Seasonal-trend decomposition based on LOESS (STL) is an efficient method for handling the intricacy and fluctuation of load data. However, different types of components reflect different levels of information and have different importance in terms of capturing temporal features. Moreover, graph convolutional network (GCN) is often utilized to capture the non-Euclidean spatial features in load data. However, as the number of network nodes increases, the generalization capacity of the GCN decreases. Therefore, a novel spatiotemporal model, namely, multitask GCN with attention-based STL (MG-ASTL), is proposed for accurate short-term load forecasting. First, a new attention-based STL method is proposed, which utilizes attention mechanism to weight different components, thus making the proposed model to focus on more important components for more effective temporal feature extraction. Second, a new multitask GCN method is proposed, which utilizes density-based spatial clustering of applications with noise (DBSCAN) to divide load data into different groups for multitask learning, so that the simple spatial patterns with fewer nodes can be learned to increase the generalization capacity. The effectiveness of the proposed model is validated on the basis of experimental results under different conditions.