Hierarchical clustering of complex energy systems using pretopology

• URL: http://arxiv.org/abs/2512.03069v1
• Date: Thu, 27 Nov 2025 08:19:50 GMT
• Title: Hierarchical clustering of complex energy systems using pretopology
• Authors: Loup-Noe Levy, Jeremie Bosom, Guillaume Guerard, Soufian Ben Amor, Marc Bui, Hai Tran,
• Abstract summary: This article attempts answering the following problematic: How to model and classify energy consumption profiles over a large distributed territory.<n>Doing case-by-case in depth auditing of thousands of buildings would require a massive amount of time and money as well as a significant number of qualified people.<n>To answer this problematic, pretopology is used to model the sites' consumption profiles and a hierarchical classification algorithm has been developed in a Python library.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article attempts answering the following problematic: How to model and classify energy consumption profiles over a large distributed territory to optimize the management of buildings' consumption? Doing case-by-case in depth auditing of thousands of buildings would require a massive amount of time and money as well as a significant number of qualified people. Thus, an automated method must be developed to establish a relevant and effective recommendations system. To answer this problematic, pretopology is used to model the sites' consumption profiles and a multi-criterion hierarchical classification algorithm, using the properties of pretopological space, has been developed in a Python library. To evaluate the results, three data sets are used: A generated set of dots of various sizes in a 2D space, a generated set of time series and a set of consumption time series of 400 real consumption sites from a French Energy company. On the point data set, the algorithm is able to identify the clusters of points using their position in space and their size as parameter. On the generated time series, the algorithm is able to identify the time series clusters using Pearson's correlation with an Adjusted Rand Index (ARI) of 1.

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