提出可解释的家用电力预测方法，兼顾准确与人类可理解性。
An explainable machine learning approach for energy forecasting at the household level
- 自定义决策树模型，提升预测可解释性。
- 在保持高精度的同时显著增强结果透明度。
- 适合需要透明决策的智能家居与能源管理场景。
电力负荷预测是实现供需平衡的关键，尽管多数研究聚焦于国家或区域层面，针对家庭层级的研究仍较少。现有机器学习方法虽能实现分项预测，但缺乏对家庭用电场景所需的可解释性。本文系统对比多种主流机器学习算法在家庭用电预测中的表现，综合评估其准确性与可解释性，并考虑该领域特有的业务挑战，如解释需求和异常值鲁棒性。为此，我们提出一种定制化决策树方法，旨在提供公平可靠的能耗估计，同时具备良好的可解释性和符合人类直觉的推理过程。实验表明，该方法在不显著牺牲准确率的前提下，大幅提升了预测结果的可解释性。该模型适用于多种商业应用场景，但对异常值的鲁棒性仍有不足。
原文摘要 · Abstract (English)
Electricity forecasting has been a recurring research topic, as it is key to finding the right balance between production and consumption. While most papers are focused on the national or regional scale, few are interested in the household level. Desegregated forecast is a common topic in Machine Learning (ML) literature but lacks explainability that household energy forecasts require. This paper specifically targets the challenges of forecasting electricity use at the household level. This paper confronts common Machine Learning algorithms to electricity household forecasts, weighing the pros and cons, including accuracy and explainability with well-known key metrics. Furthermore, we also confront them in this paper with the business challenges specific to this sector such as explainability or outliers resistance. We introduce a custom decision tree, aiming at providing a fair estimate of the energy consumption, while being explainable and consistent with human intuition. We show that this novel method allows greater explainability without sacrificing much accuracy. The custom tree methodology can be used in various business use cases but is subject to limitations, such as a lack of resilience with outliers.
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