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Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads

2023-01-30 15:11:46
Julien Leprince, Henrik Madsen, Jan Kloppenborg Møller, Wim Zeiler

Abstract

Optimal decision-making compels us to anticipate the future at different horizons. However, in many domains connecting together predictions from multiple time horizons and abstractions levels across their organization becomes all the more important, else decision-makers would be planning using separate and possibly conflicting views of the future. This notably applies to smart grid operation. To optimally manage energy flows in such systems, accurate and coherent predictions must be made across varying aggregation levels and horizons. With this work, we propose a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors and established hierarchical reconciliation taxonomy. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal hierarchies under a common frame is initially defined. Next, a coherency-informed hierarchical learner is developed built upon a custom loss function leveraging optimal reconciliation methods. Coherency of the produced hierarchical forecasts is then secured using similar reconciliation technics. The outcome is a unified and coherent forecast across all examined dimensions. The method is evaluated on two different case studies to predict building electrical loads across spatial, temporal, and spatio-temporal hierarchies. Although the regressor natively profits from computationally efficient learning, results displayed disparate performances, demonstrating the value of hierarchical-coherent learning in only one setting. Yet, supported by a comprehensive result analysis, existing obstacles were clearly delineated, presenting distinct pathways for future work. Overall, the paper expands and unites traditionally disjointed hierarchical forecasting methods providing a fertile route toward a novel generation of forecasting regressors.

Abstract (translated)

优化决策迫使我们在不同的预测截止日期上预期未来。然而,在许多领域,将来自不同组织、抽象程度不同的预测整合在一起变得越来越重要,否则决策者将会使用独立且可能相互矛盾的未来观点来规划。这特别适用于智能电网的操作。为了优化管理这样的系统中的能量流动,必须在所有可能的聚合水平和预测截止日期上做出准确且一致的预测。通过这项工作,我们提出了一种独特的多维层次预测方法,基于结构 informed 的机器学习倒退者,并建立了层次和解调分类树。一个通用的层次树构建框架被最初定义,该框架在一个共同框架下协调了空间和时间层次。然后,基于自定义损失函数,开发了一种协调的多维层次学习,使用类似的和解调技术确保了生成的层次预测的一致性。结果是一个统一且一致的预测在所有被检查的维度上呈现。方法在两个不同 case 研究中进行评估,以预测空间、时间、空间-时间层次上的建筑电力负载。虽然倒退者天生从计算高效的学习中获得利润,但结果显示了不一致的表现,证明了层次-协调学习在一个特定环境中的价值。然而,通过全面的结果分析,现有的障碍被明确定义,为未来的工作提供了不同的路径。总体而言,论文扩展并统一了传统的层次预测方法,为新的倒退者 generation 提供了一种有益的路径。

URL

https://arxiv.org/abs/2301.12967

PDF

https://arxiv.org/pdf/2301.12967.pdf


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