Hierarchical clustering of complex energy systems using pretopology

TL;DR

This paper introduces a hierarchical clustering method based on pretopology to classify energy consumption profiles across large territories, enabling automated and efficient management recommendations.

Contribution

It develops a novel multi-criterion hierarchical classification algorithm using pretopology properties, applied to real and synthetic energy consumption data.

Findings

Successfully clusters point data based on position and size.

Accurately identifies time series clusters with high ARI score.

Demonstrates effectiveness on real energy consumption data.

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.