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Montet Frédéric

Collaborateur scientifique HES - Doctorant

Hauptkompetenzen

Collaborateur scientifique HES - Doctorant

Büro: HEIA_D20.19

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH

Institut
iCoSys - Institut des systèmes complexes

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Laufend

Facility 4.0

Rolle: Hauptgesuchsteller/in

Financement: HEIA-FR (Smart Living Lab research program)

Description du projet :

Gestion 4.0 des bâtiments – services innovants pour le « monitoring » de la performance et le « facility management »

Ce projet s’inscrit dans le contexte général de la transition énergétique et de la diminution de l’impact environnemental du secteur du bâtiment qui représente 1⁄3 des émissions de CO2 en Suisse et environ 45% de la consommation énergétique. La question du monitoring et de l’optimisation de la performance des 2.3 millions de bâtiments en Suisse est donc centrale.

Pour les exploitants de parc immobilier, un obstacle à la maîtrise et à l’augmentation de la performance dans le domaine du Facility Management (FM) est le manque de données issues des bâtiments ainsi que de ressources nécessaires à leur analyse. D’autre part, les systèmes de gestion technique ne sont souvent pas conçus pour le stockage et l’analyse d’historiques de données.

Le projet FACILITY 4.0 vise l’évaluation et la mise au point de services de monitoring et de gestion des bâtiments déployables à grande échelle et basés sur les techniques récentes de la science des données incluant l’IA, le machine learning, le cloud et l’Internet des objets. Plus spécifiquement, des services d’analyse, de maintenance prédictive, d’alerte ou de prévision sont ciblés dans FACILITY 4.0.

Un groupe de co-création constitué de divers partenaires économiques et scientifiques a pour rôle d’identifier et de spécifier les services ICT testés durant le projet.

Forschungsteam innerhalb von HES-SO: Montet Frédéric

Durée du projet: 01.09.2019 - 31.12.2021

Url des Projektstandortes: https://icosys.ch/facility-4-0

Statut: Laufend

2024

Autodigit-RAD: Towards an automation of the radon’s concentration dataflow in a new and innovative building

Wissenschaftlicher Artikel

Rey Joan Frédéric, Cesari Matias, Schoenenweid Marion, Montet Frédéric, Mauro Gandolla, Leevan Bonvin, Bourquin Vincent, Claude-Alain Jaccot, Justine Roman, Sebastian Duque Mahecha, Sergi Aguacil Moreno, Hennebert Jean, Goyette Pernot Joëlle

Journal of Physics: Conference Series, 2024 , vol. 2600, no 102008

Link zur Publikation

Zusammenfassung:

Radon is a noble, natural, and radioactive gas coming mainly from the ground which might accumulate indoors and lead each year to 200-300 deaths from lung cancer in Switzerland. A brand new and innovative living lab will be built as of 2023 in Fribourg (Switzerland) which will allow to tackle the built environment and the relationship with its occupants. Among a large panel of environmental parameters, radon gas will be continuously monitored under and around the building as well as in the building envelope. This paper aims to present the overall process of the radon dataflow: 1) design of the sensor probes, 2) implementation of the radon sensor probes in the ground and 3) go-live with the data sharing platform with the building users. Such an infrastructure will bring the opportunity to researchers to lead new and innovative radon-related research.

2022

Energy performance estimation for large building portfolios with machine learning-based techniques

Wissenschaftlicher Artikel ArODES

Frédéric Montet, Alessandro Pongelli, Joanathan Rial, Stefanie Schwab, Jean Hennebert, Thomas Jusselme

Acta Polytechnica CTU Proceedings, 2022, vol. 38: Central Europe towards sustainable building 2022 (CESB22), pp. 90-96

Link zur Publikation

Zusammenfassung:

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature. This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system. This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

2021

Big Building Data 2.0 :

Wissenschaftlicher Artikel ArODES

a big data platform for smart buildings

Lucy Linder, Frédéric Montet, Jean Hennebert, Jean-Philippe Bacher

Journal of Physics: Conference Series, 2021, no. 2042, article no. 012016

Link zur Publikation

Zusammenfassung:

The modern built environment is now connected. Multiple software and protocols are used in buildings of many kinds, thus creating a fascinating and heterogeneous environment. Within this context, applied research can be complicated and would benefit from a single data location across projects and users. The first version of BBData tried to solve this problem, BBData v2.0 is an update with a better-defined scope and a new codebase. The solution has been open sourced and simplified with a full software rewrite. Its components are now state-of-the-art and proven to be stable in industrial settings. The achieved performances have been thoroughly tested. Together with its new architecture, BBData v2.0 now accommodates the needs of modern experiments; efficient for simple proof of concepts while keeping the possibility to scale up to city-level projects. This flexibility makes BBData a good candidate for research while being able to scale in production settings.

Prediction of domestic hot water temperature in a district heating network

Wissenschaftlicher Artikel ArODES

Frédéric Montet, Lorenz Rychener, Alessandro Pongelli, Jean Hennebert, Jean-Philippe Bacher

Journal of Physics: Conference Series, 2021, no. 2042, article no. 012026

Link zur Publikation

Zusammenfassung:

With the fourth generation of district heating networks in sight, opportunities are rising for better services and optimized planning of energy production. Indeed, the more intensive data collection is expected to allow for load prediction, customer profiling, etc. In this context, our work aims at a better understanding of customer profiles from the captured data. Given the variety of households, such profiles are difficult to capture. This study explores the possibility to predict domestic hot water (DHW) usage. Such prediction is made challenging due to the presence of two components in the signal, the first one bound to the physical properties of the DHW distribution system, the second one bound to the human patterns related to DHW consumption. Our contributions include (1) the analysis of recurrent neural network architectures based on GRU, (2) the inclusion of state-based labels inferred in an unsupervised way to simulate domain knowledge, (3) the comparison of different features. Results show that the physical contribution in the signal can be forecasted successfully across households. On the contrary, the stochastic "human" component is harder to predict and would need further research, either by improving the modelling or by including alternate signals.

2020

Architecture Proposal for Machine Learning Based Industrial Process Monitoring

Wissenschaftlicher Artikel

Rychener Lorenz, Montet Frédéric, Hennebert Jean

Procedia Computer Science, 2020 , vol. 170, pp. 648-655

Link zur Publikation

Zusammenfassung:

In this paper, we take the perspective of IT system architects and analyse the implications of the inclusion of ML components into a traditional anomaly detection systems. Through a prototype that we deployed for chemical reactors, our findings are that such ML components are impacting drastically the architecture of classical alarm systems.

2024

Integration of large language models intro control systems for shared appliances

Konferenz ArODES

Frédéric Montet, Karl Löwenmark, Marcus Liwicki, Fredrik Sandin, Jean Hennebert

Proceedings of AMBIENT 2024, The Fourteenth International Conference on Ambient Computing, Applications, Services and Technologies, 29 September-3 October 2024, Venice, Italy

Link zur Konferenz

Zusammenfassung:

Large Language Model (LLM)-powered chatbot agents have proven to be immensely useful in tasks, such as writing and generating essays, code, and academic text. By using frameworks such as LangChain, agents can be equipped with tools to access and analyse custom data, which facilitates bespoke applications, such as customer service agents with access to internal documents and tailored reasoning. While the focus of such applications has mainly centered around textual content, custom toolboxes could also enable agents to act in completely different use cases, for instance control theory. Nevertheless, given the non-deterministic nature of LLMs, merging them with deterministic software implies challenges in applied contexts such as privacy, multi-user interactions, and consistency. To pave the way to reliable LLM usage in various contexts, this work provides the foundation for expanding the use of LLM agents to the domain of control systems and human-centric automation. An agent-based architecture is proposed, which is then implemented within the context of a shared space heating system controlled by three personas. Finally, we evaluate the capacity of the system to deal with scenarios such as normality, erratic user behavior, conflicts of interest, and system limitations. The findings of this study highlight the potential benefits and challenges of using LLMs for appliance control. All code is made public at https://github.com/fredmontet/llm-cps-ac to facilitate further research.

Managing and optimizing a set of PV installations at the low-voltage grid level :

Konferenz ArODES

a data-driven concept through machine learning techniques

Thibaud Alt, Beat Wolf, Jean-Philippe Bacher, Frédéric Montet

Proceedings of the 7th European Grid Service Market Symposium, 1-2 July 2024, Lucerne, Switzerland

Link zur Konferenz

Zusammenfassung:

This paper proposes a data-driven approach to managing and optimizing a set of photovoltaic (PV) installations by exploiting the possibilities of spatio-temporal modeling and machine learning techniques. Given the variable nature of solar energy production, optimizing PV installations for maximum output and efficiency is crucial. The aim is to identify trends, patterns, challenges, and opportunities for improvement in the operation of multi-site PV systems as well as to provide information for optimal management of the lowvoltage network. A diverse array of methods are compared to forecast energy production, detect declines in system performance and refine maintenance scheduling. This study contributes to the growing field of renewable energy management by showcasing the effectiveness of ML models in optimizing a set of PV systems. It sets the stage for future progress in incorporating renewable energy sources into the electrical grid.

2023

Energy performance certificate estimation at large scale based on open data

Konferenz ArODES

Frédéric Montet, Alessandro Pongelli, Stefanie Schwab, Mylène Devaux, Thomas Jusselme, Jean Hennebert

Proceedings of cisbat 2023, the built environment in transititon, Hybrid International Scientific Conference, 13-15 September 2023, Lausanne, Switzerland

Link zur Konferenz

Zusammenfassung:

This paper presents an innovative methodology for enhancing energy efficiency assessment procedures in the built environment, with a focus on the Switzerland’s Energy Strategy 2050. The current methodology necessitates intensive expert surveys, leading to substantial time and cost implications. Also, such a process can’t be scaled to a large number of buildings. Using machine learning techniques, the estimation process is augmented and exploit open data resources. Utilizing a robust dataset exceeding 70’000 energy performance certificates (CECB), the method devises a two-stage ML approach to forecast energy performance. The first phase involves data reconstruction from online repositories, while the second employs a regression algorithm to estimate the energy efficiency. The proposed approach addresses the limitations of existing machine learning methods by offering finer prediction granularity and incorporating readily available data. The results show a commendable degree of prediction accuracy, particularly for single-family residences. Despite this, the study reveals a demand for further granular data, and underlines privacy concerns associated with such data collection. In summary, this investigation provides a significant contribution to the enhancement of energy efficiency assessment methodologies and policymaking.

Autodigit-RAD :

Konferenz ArODES

towards an automation of the radon’s concentration dataflow in a new and innovative building

Joan Frédéric Rey, Matias Cesari, Frédéric Montet, M. Gandolla, Leewan Bonvin, Vincent Bourquin, C. L. Jacot, Justine Roman, Sebastian Duque Mahecha, Sergi Aguacil Moreno, Jean Hennebert, Joëlle Goyette Pernot

Journal of Physics: Conference Series ; Proceedings of cisbat 2023, the built environment in transititon, Hybrid International Scientific Conference, 13-15 September 2023, Lausanne, Switzerland

Link zur Konferenz

Zusammenfassung:

2021

Energy performance estimation for large building portfolios with machine learning-based techniques

Konferenz ArODES

Frédéric Montet, Alessandro Pongelli, Rial, Stefanie Schwab, Jean Hennebert, Thomas Jusselme

Proceedings of Central Europe towards Sustainable Building 2022, International Scientific Conference, 4-6 July 2022, Prague, Czech Republic; Acta Polytechnica

Link zur Konferenz

Zusammenfassung:

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature. This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system. This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70’000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

Errungenschaften

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

Montet Frédéric

Collaborateur scientifique HES - Doctorant

Hauptkompetenzen

Machine Learning

Développement web

Software Engineering

Collaborateur scientifique HES - Doctorant

Gestion 4.0 des bâtiments – services innovants pour le « monitoring » de la performance et le « facility management »

PEOPLE@HES-SO Verzeichnis der Mitarbeitenden und Kompetenzen

Montet Frédéric

Collaborateur scientifique HES - Doctorant

Hauptkompetenzen

Machine Learning

Développement web

Software Engineering

Collaborateur scientifique HES - Doctorant

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen