PhaCIA−TCNs: Short−Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention

Abstract

Short-term load forecasting (STLF) is an important task in modern smart grids, which reduces energy waste and enhances the reliability of the power system. RNN-based models are the most widely used deep-learning-based methods for STLF, but their forecasting performance is limited due to an unbalanced non-linearity problem. Temporal convolution networks (TCNs) are thus proposed to remedy this problem, however, TCNs have two shortcomings, i.e., redundant convolutional operation and equal input importance problems. Therefore, we propose a novel TCN-based backbone model, called PhaCIA-TCNs, to achieve a more accurate short-term load forecasting, where parallel hybrid activated convolution (PhaC) and input attention (IA) are proposed to resolve the above problems of TCNs. PhaC shortens the convolutional learning path and time-cost of TCNs while maintaining superior feature learning capabilities, and IA is used to highlight important input elements while depress irrelevant ones. Extensive experimental results show that (i) PhaCIA-TCNs significantly outperform all state-of-the-art RNN-based and TCNs-based baselines in forecasting-error-based evaluation metrics on all datasets; (ii) PhaC and IA are both effective and essential for PhaCIA-TCN to achieve the superior STLF performances; and (iii) despite achieving better forecasting accuracies, the training time-cost of PhaCIA-TCN is similar (and sometimes even lower) to the existing TCNs-based solution.