Accurate electricity demand forecasts play a crucial role in sustainable
power systems. To enable better decision-making especially for demand
flexibility of the end-user, it is necessary to provide not only accurate but
also understandable and actionable forecasts. To provide accurate forecasts
Global Forecasting Models (GFM) trained across time series have shown superior
results in many demand forecasting competitions and real-world applications
recently, compared with univariate forecasting approaches. We aim to fill the
gap between the accuracy and the interpretability in global forecasting
approaches. In order to explain the global model forecasts, we propose Local
Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF),
a local explainer framework that produces k-optimal impact rules for a
particular forecast, considering the global forecasting model as a black-box
model, in a model-agnostic way. It provides different types of rules that
explain the forecast of the global model and the counterfactual rules, which
provide actionable insights for potential changes to obtain different outputs
for given instances. We conduct experiments using a large-scale electricity
demand dataset with exogenous features such as temperature and calendar
effects. Here, we evaluate the quality of the explanations produced by the
LIMREF framework in terms of both qualitative and quantitative aspects such as
accuracy, fidelity, and comprehensibility and benchmark those against other
local explainers.

本研究探索了利用 Local Interpretable Model-agnostic Rule-based Explanations for Forecasting（LIMREF）等局部解释框架，解释全球预测模型中的电力需求预测，确保预测精度和可解释性，实现更好的决策制定，同时考虑了包括温度和日历效应在内的外生影响因素。