Spatial–temporal information model-based load current interval prediction for transmission lines

Abstract

Load current interval prediction (LCIP) plays an increasingly significant role in transmission lines load demand uncertainty assessment and becomes necessary for power system dispatch and management. However, the stochastic, volatility and non-stationary characteristics of load demand bring challenges to high-quality prediction intervals (PIs) estimation. In this article, a hybrid LCIP model based on Spatial–Temporal Attention integrating Temporal Convolutional Network (STATCN) and residual estimation is proposed. First, to diminish the uncertainty in the prediction process, a spatial-partitioned method and a temporal-phased method are proposed for decoupling the spatial and temporal correlations among transmission lines. Then, a spatial–temporal attention mechanism is fused in temporal convolutional networks to enhance the sensitivity of ensemble model for important information from the temporal and spatial feature dimensions. Finally, in the STATCN interval prediction model, a residual estimation optimization strategy is designed to eliminate quantile crossings and ensure the validity of the prediction interval results. The performance of the proposed prediction framework is verified by actual numerical simulation. The results demonstrate that our proposed method has higher quality PIs compared to the existing state-of-the-art interval prediction methods.