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【Applied Energy 最新原创论文】基于分层学习的建筑负荷空间预测方法

AEii国际应用能源  · 公众号  ·  · 2023-07-09 18:30

正文

原文信息:

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

原文链接:

https://www.sciencedirect.com/science/article/pii/S0306261923008747

Highlights

Multi-dimensional hierarchy definition

• Hierarchical forecasting with machine learning

Custom coherency loss function built from optimal reconciliation methods

Deep-learning model design and hyper parameter tuning

Smart-building electrical load forecasting from open dataset.

摘要

人们通常期望获得最优决策方式,而这驱动决策者从不同的尺度开展预测。在许多领域,将集群中多个时间尺度和特征的预测结合在一起变得更加重要,否则决策者不得不使用单独且可能相互冲突的预测结果进行决策。为此,文章提出了一种基于结构信息机器学习回归器(structurally-informed machine-learning regressors)的多维分层预测算法。首先,定义了多维层次结构的通用形式,用以协调空间和时间维度。然后,利用最佳协调方法,基于自定义的损失函数设计了基于一致性的分层学习器。使用类似的协调技术来确保所生成的分层预测的一致性,从而使决策者对未来服务一致的决策具有统一的评估。采用两个不同的算例评估方法的有效性,用以预测跨空间、时间和时空层次结构的建筑电力负荷。仿真结果证明了层次一致性学习的性能。同时,仿真结果明确界定了现有的研究不足,为未来的工作提供了参考。总体而言,该文章扩展并统一了以往脱节的分层预测方法,为新一代预测回归器提供了有效的途径。

更多关于" load forecasting "的研究请见:

https://www.sciencedirect.com/search?pub=Applied%20Energy&cid=271429&qs=load%20forecasting

Abstr act

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. To this end, this work proposes a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal dimensions 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. The coherency of the produced hierarchical forecasts is then secured using similar reconciliation techniques, granting decision-makers a common view of the future serving aligned decision-making. 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, 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.

Keywords

Hierarchical forecasting

Coherency

Spatio-temporal dimensions

Deep learning

Smart building

Graphics

Fig. 3. Exemplified illustrations of hierarchical derivations of summation matrix.







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