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Application of Pretopological Hierarchical Clustering for Buildings Portfolio

Topics: AI-driven approaches for smart buildings; Big Data and Urban Data Analytics; Digital Service Innovation for Cities; Energy Management Systems (EMS); Energy Monitoring; Energy-Aware Process Optimisation; Greener Systems Planning and Design; Internet of Things for Sustainability; Mechanisms for Motivating Behaviour Change; Optimization Techniques for Efficient Energy Consumption; Power Consumption; Research and Innovation in Creative Industries; Smart Sensor Networks and Applications; Urban Monitoring and Optimization

Authors: Loup-Noé Lévy 1 ; 2 ; Jérémie Bosom 1 ; 3 ; Guillaume Guerard 4 ; Soufian Ben Amor 2 ; Marc Bui 3 and Hai Tran 1

Affiliations: 1 Energisme, 88 Avenue du Général Leclerc, 92100 Boulogne-Billancourt, France ; 2 LI-PARAD Laboratory EA 7432, Versailles University, 55 Avenue de Paris, 78035 Versailles, France ; 3 EPHE, PSL Research University, 4-14 Rue Ferrus, 75014 Paris, France ; 4 De Vinci Research Center, Pole Universitaire Léonard de Vinci, 12 Avenue Léonard de Vinci, 92400 Courbevoie, France

Keyword(s): Artificial Intelligence, Data Analysis, Clustering Algorithms, Pretopology.

Abstract: Our paper deals with the problem of the comparison of heterogeneous energy consumption profiles for energy optimization. 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 in order to establish a relevant and effective recommendations system. Comparing sites to extract similar profiles refers to a machine learning set of methods called clustering. To answer this problematic, pretopology is used to model the sites’ consumption profiles and a multi-criteria hierarchical clustering algorithm, using the properties of pretopological space, has been developed using a Python library. The pretopological hierarchical clustering algorithm is able to identify the clusters and provide a hierarchy between complex items. Tested on benchmarks of generated time series (from literature and from french energy company), the algorithm is able to id entify the clusters using Pearson’s correlation with an Adjusted Rand Index of 1 and returns relevant results on real energy systems’ consumption data. (More)

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Paper citation in several formats:
Lévy, L.; Bosom, J.; Guerard, G.; Ben Amor, S.; Bui, M. and Tran, H. (2021). Application of Pretopological Hierarchical Clustering for Buildings Portfolio. In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS, ISBN 978-989-758-512-8; ISSN 2184-4968, pages 228-235. DOI: 10.5220/0010485802280235

@conference{smartgreens21,
author={Loup{-}Noé Lévy. and Jérémie Bosom. and Guillaume Guerard. and Soufian {Ben Amor}. and Marc Bui. and Hai Tran.},
title={Application of Pretopological Hierarchical Clustering for Buildings Portfolio},
booktitle={Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS,},
year={2021},
pages={228-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010485802280235},
isbn={978-989-758-512-8},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS,
TI - Application of Pretopological Hierarchical Clustering for Buildings Portfolio
SN - 978-989-758-512-8
IS - 2184-4968
AU - Lévy, L.
AU - Bosom, J.
AU - Guerard, G.
AU - Ben Amor, S.
AU - Bui, M.
AU - Tran, H.
PY - 2021
SP - 228
EP - 235
DO - 10.5220/0010485802280235

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