SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric
  Load Forecasting under Extreme Events
Hengbo Liu Ziqing Ma Linxiao Yang Tian Zhou Rui Xia Yi Wang Qingsong Wen Liang Sun
Accurate prediction of electric load is crucial in power grid planning and
management. In this paper, we solve the electric load forecasting problem under
extreme events such as scorching heats. One challenge for accurate forecasting
is the lack of training samples under extreme conditions. Also load usually
changes dramatically in these extreme conditions, which calls for interpretable
model to make better decisions. In this paper, we propose a novel forecasting
framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which
ensembles long-term trend, short-term trend, and period modelings to capture
temporal characteristics in different components. The external variable
triggered loss is proposed for the imbalanced learning under extreme events.
Furthermore, Generalized Additive Model (GAM) is employed in the framework for
desirable interpretability. The experiments on both Central China electric load
and public energy meters from buildings show that the proposed SaDI framework
achieves average 22.14% improvement compared with the current state-of-the-art
algorithms in forecasting under extreme events in terms of daily mean of
normalized RMSE. Code, Public datasets, and Appendix are available at:
https://doi.org/10.24433/CO.9696980.v1 .