Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep learning approaches have been proposed to perform this task, most of them either overlook the essential spatial correlations across households or fail to scale to individualized prediction, making them less effective for accurate fine-grained user-level prediction. In addition, due to the dynamic and uncertain nature of energy usage caused by various factors such as extreme weather events, quantifying uncertainty for reliable prediction is also significant, but it has not been fully explored in existing work. In this paper, we propose a unified framework called TrustEnergy for accurate and reliable user-level energy usage prediction. There are two key technical components in TrustEnergy, (i) a Hierarchical Spatiotemporal Representation module to efficiently capture both macro and micro energy usage patterns with a novel memory-augmented spatiotemporal graph neural network, and (ii) an innovative Sequential Conformalized Quantile Regression module to dynamically adjust uncertainty bounds to ensure valid prediction intervals over time, without making strong assumptions about the underlying data distribution. We implement and evaluate our TrustEnergy framework by working with an electricity provider in Florida, and the results show our TrustEnergy can achieve a 5.4% increase in prediction accuracy and 5.7% improvement in uncertainty quantification compared to state-of-the-art baselines.

翻译：能耗预测对于电网管理、基础设施规划和灾害响应等多种实际应用具有重要意义。尽管已有大量深度学习方法被提出用于执行此任务，但其中大多数方法要么忽略了家庭间关键的空间相关性，要么无法扩展至个体化预测，导致其在精确细粒度用户级预测方面效果有限。此外，由于极端天气事件等多种因素导致的能耗动态性和不确定性，量化不确定性以实现可靠预测同样至关重要，但现有工作尚未对此进行充分探索。本文提出了一种名为TrustEnergy的统一框架，用于实现精确可靠的用户级能耗预测。TrustEnergy包含两个关键技术组件：（i）一种分层时空表征模块，通过新型记忆增强时空图神经网络有效捕获宏观与微观能耗模式；（ii）一种创新的序列保形化分位数回归模块，可动态调整不确定性边界，确保随时间推移产生有效的预测区间，而无需对底层数据分布做出强假设。我们通过与佛罗里达州一家电力供应商合作实施并评估TrustEnergy框架，结果表明，相较于最先进的基线方法，TrustEnergy可实现预测精度提升5.4%，不确定性量化改进5.7%。