A Unifying Framework of Attention-based Neural Load Forecasting
Jing Xiong Yu Zhang
Accurate load forecasting is critical for reliable and efficient planning and
operation of electric power grids. In this paper, we propose a unifying deep
learning framework for load forecasting, which includes time-varying feature
weighting, hierarchical temporal attention, and feature-reinforced error
correction. Our framework adopts a modular design with good generalization
capability. First, the feature-weighting mechanism assigns input features with
temporal weights. Second, a recurrent encoder-decoder structure with
hierarchical attention is developed as a load predictor. The hierarchical
attention enables a similar day selection, which re-evaluates the importance of
historical information at each time step. Third, we develop an error correction
module that explores the errors and learned feature hidden information to
further improve the model's forecasting performance. Experimental results
demonstrate that our proposed framework outperforms existing methods on two
public datasets and performance metrics, with the feature weighting mechanism
and error correction module being critical to achieving superior performance.
Our framework provides an effective solution to the electric load forecasting
problem, which can be further adapted to many other forecasting tasks.