Interpretable Short-Term Load Forecasting via Multi-Scale Temporal
  Decomposition
Yuqi Jiang Yan Li Yize Chen
Rapid progress in machine learning and deep learning has enabled a wide range
of applications in the electricity load forecasting of power systems, for
instance, univariate and multivariate short-term load forecasting. Though the
strong capabilities of learning the non-linearity of the load patterns and the
high prediction accuracy have been achieved, the interpretability of typical
deep learning models for electricity load forecasting is less studied. This
paper proposes an interpretable deep learning method, which learns a linear
combination of neural networks that each attends to an input time feature. We
also proposed a multi-scale time series decomposition method to deal with the
complex time patterns. Case studies have been carried out on the Belgium
central grid load dataset and the proposed model demonstrated better accuracy
compared to the frequently applied baseline model. Specifically, the proposed
multi-scale temporal decomposition achieves the best MSE, MAE and RMSE of 0.52,
0.57 and 0.72 respectively. As for interpretability, on one hand, the proposed
method displays generalization capability. On the other hand, it can
demonstrate not only the feature but also the temporal interpretability
compared to other baseline methods. Besides, the global time feature
interpretabilities are also obtained. Obtaining global feature
interpretabilities allows us to catch the overall patterns, trends, and
cyclicality in load data while also revealing the significance of various
time-related features in forming the final outputs.