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

Authors :
Leprince, Julien
Madsen, Henrik
Møller, Jan Kloppenborg
Zeiler, Wim
Source :
Applied Energy. Oct2023, Vol. 348, pN.PAG-N.PAG. 1p.
Publication Year :
2023

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. 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. • 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 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03062619
Volume :
348
Database :
Academic Search Index
Journal :
Applied Energy
Publication Type :
Academic Journal
Accession number :
170087874
Full Text :
https://doi.org/10.1016/j.apenergy.2023.121510