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.

翻译：电力预测是一个反复出现的研究主题，因为它是实现生产与消费之间适当平衡的关键。虽然大多数论文关注国家或区域尺度，但鲜有研究聚焦家庭层面。在机器学习文献中，分项预测是一个常见主题，但缺乏家庭能源预测所需的可解释性。本文专门针对家庭层面电力使用预测的挑战。本文对比了常见机器学习算法在家庭电力预测中的应用，权衡了包括准确性和可解释性在内的优缺点，并采用公认的关键指标进行评估。此外，本文还结合该领域特有的业务挑战（如可解释性或异常值鲁棒性）对这些算法进行了评估。我们提出了一种定制决策树，旨在提供能源消耗的合理估计，同时保持可解释性并与人类直觉一致。研究表明，这种新方法在不显著牺牲准确性的前提下实现了更强的可解释性。该定制树方法可应用于多种业务场景，但仍存在局限性，例如对异常值缺乏鲁棒性。