Temporal structure-preserving transformer for industrial load forecasting.

Abstract

Accurate power load forecasting in industrial parks is crucial for optimizing energy management and operational efficiency. Existing models struggle with industrial load series' complex, multi-target nature and the need to integrate diverse exogenous variables. This paper introduces the Temporal Structure-Preserving Transformer (TSPT), a novel architecture that addresses these challenges by decomposing multi-target series into univariate series, enabling parallel processing and integrating exogenous data. The TSPT model incorporates the Gated Feature Fusion (GFF), which learns to capture multiscale temporal patterns from each target sequence and exogenous factors by preserving the temporal structure of the series. This parallel processing and the structure-preserving transformations allow TSPT to effectively integrate domain-specific knowledge, such as weather, production, and efficiency data, enhancing its forecasting performance. Comprehensive experiments on a real-world industrial park dataset demonstrate TSPT's superiority over state-of-the-art methods in handling complex, multi-target forecasting tasks with integrated exogenous variables. The proposed approach offers a pathway for scalable and accurate load forecasting in industrial settings, improving energy management and operational decision-making.