Hierarchical Clustering of Complex Energy Systems Using Pretopology

2025-11-27

SMARTGREENS/VEHITS

AI Summary

Modeling and automatically classifying energy consumption patterns across large-scale distributed regional buildings remains challenging due to spatial heterogeneity, temporal dynamics, and limited physical interpretability. Method: This paper proposes a multi-criteria hierarchical clustering method grounded in pre-topological space properties. It jointly models energy consumption profiles using spatial, temporal, and statistical features; employs pre-topological structures to encode shape and connectivity; and integrates Pearson correlation analysis for multi-dimensional feature fusion. An open-source Python library is developed to support point-set, time-series, and real-world energy data processing. Contribution/Results: The work pioneers the application of pre-topology to energy pattern recognition, enhancing both clustering accuracy and physical interpretability. Experiments demonstrate exact spatial cluster recovery on synthetic data, perfect time-series clustering (adjusted Rand index = 1.0), and robust grouping of 400 real-world energy sites—enabling a new paradigm for intelligent, low-intervention energy management.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

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.

Problem

• Model energy consumption profiles for large distributed territories
• Automate classification to optimize building consumption management
• Develop hierarchical clustering using pretopology and multi-criterion algorithms

Innovation

• Pretopology models energy consumption profiles
• Hierarchical classification algorithm uses pretopological properties
• Python library implements multi-criterion clustering for optimization