Multi-Source Short-Term Electricity Load Forecasting with Explainable Attention-Enhanced PatchTST
TL;DR
This work presents an interpretable forecasting system that merges heterogeneous data streams with an attention-augmented PatchTST backbone, and achieves competitive results (MAE 16.46) on the benchmark.
Abstract
Reliable short-term prediction of electricity consumption is essential for dispatching power grids and incorporating renewable sources. This work presents an interpretable forecasting system that merges heterogeneous data streams with an attention-augmented PatchTST backbone. Real-world meteorological records from NOAA are combined with electricity demand data, and we design time-adaptive correlation descriptors alongside a binary extreme-weather flag. A one-dimensional Coordinate Attention (CA) mechanism, originally developed for image analysis, is integrated into the PatchTST encoder to enable channel-wise reweighting. Using the UCI Electricity Load dataset for customer MT_329 (2011-2012), our CA-PatchTST attains a MAE of 16.46, surpassing LSTM (18.40) and the unmodified PatchTST (17.59). Ablation tests indicate that incorporating meteorological information is the primary driver of accuracy gain, while the CA module contributes moderate improvement and, crucially, offers visualizable attention maps that clarify the model's behavior under severe weather. The fusion of real NOAA observations lifts forecasting performance by over 50% relative to load-only models. The dynamic correlation features effectively capture seasonal reversals in load–weather dependency across months. Overall, the proposed approach achieves competitive results (MAE 16.46) on the benchmark.