OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK

Sylvain Le Corff; Alain Champagne; Maurice Charbit; Gilles Noziere; Eric Moulines

doi:10.1109/DSW.2018.8439901

OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK

Sylvain Le Corff
, Alain Champagne
, Maurice Charbit
, Gilles Noziere
, Eric Moulines

Laboratoire de Mathématiques d'Orsay

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

3 Citations (Scopus)

Résumé

This paper proposes a new methodology to reduce energy consumptions in large buildings while simultaneously optimizing thermal comfort. The model designed with an energy simulation program is calibrated by the Covariance Matrix Adaptation Evolutionary Strategy using observations including consumptions, inside temperatures and comfort measurements such as CO₂ emissions obtained with sensors displayed in the building. The temperatures inside the building and the energy consumptions predicted by the calibrated model during a new time period are then compared to the corresponding observations. The model is then used to find a set of Pareto optimal schedulings and tunings of the building management system in terms of energy loads and thermal comfort using multi-objective optimization.

langue originale	Anglais
titre	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	41-45
Nombre de pages	5
ISBN (imprimé)	9781538644102
Les DOIs	https://doi.org/10.1109/DSW.2018.8439901
état	Publié - 17 août 2018
Modification externe	Oui
Evénement	2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Suisse Durée: 4 juin 2018 → 6 juin 2018

Série de publications

Nom	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Une conférence

Une conférence	2018 IEEE Data Science Workshop, DSW 2018
Pays/Territoire	Suisse
La ville	Lausanne
période	4/06/18 → 6/06/18

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre

Accès au document

10.1109/DSW.2018.8439901

Contient cette citation

Le Corff, S., Champagne, A., Charbit, M., Noziere, G., & Moulines, E. (2018). OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK. Dans 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings (p. 41-45). Article 8439901 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSW.2018.8439901

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title = "OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK",

abstract = "This paper proposes a new methodology to reduce energy consumptions in large buildings while simultaneously optimizing thermal comfort. The model designed with an energy simulation program is calibrated by the Covariance Matrix Adaptation Evolutionary Strategy using observations including consumptions, inside temperatures and comfort measurements such as CO2 emissions obtained with sensors displayed in the building. The temperatures inside the building and the energy consumptions predicted by the calibrated model during a new time period are then compared to the corresponding observations. The model is then used to find a set of Pareto optimal schedulings and tunings of the building management system in terms of energy loads and thermal comfort using multi-objective optimization.",

keywords = "Evolutionary algorithms, Multi-objective optimization, Pareto optimality, Sustainable use of energy, thermal comfort",

author = "\{Le Corff\}, Sylvain and Alain Champagne and Maurice Charbit and Gilles Noziere and Eric Moulines",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Data Science Workshop, DSW 2018 ; Conference date: 04-06-2018 Through 06-06-2018",

year = "2018",

month = aug,

day = "17",

doi = "10.1109/DSW.2018.8439901",

language = "English",

isbn = "9781538644102",

series = "2018 IEEE Data Science Workshop, DSW 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2018 IEEE Data Science Workshop, DSW 2018 - Proceedings",

}

Le Corff, S, Champagne, A, Charbit, M, Noziere, G & Moulines, E 2018, OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK. Dans 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings., 8439901, 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., p. 41-45, 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Suisse, 4/06/18. https://doi.org/10.1109/DSW.2018.8439901

OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK. / Le Corff, Sylvain; Champagne, Alain; Charbit, Maurice et al.
2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 41-45 8439901 (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK

AU - Le Corff, Sylvain

AU - Champagne, Alain

AU - Charbit, Maurice

AU - Noziere, Gilles

AU - Moulines, Eric

PY - 2018/8/17

Y1 - 2018/8/17

N2 - This paper proposes a new methodology to reduce energy consumptions in large buildings while simultaneously optimizing thermal comfort. The model designed with an energy simulation program is calibrated by the Covariance Matrix Adaptation Evolutionary Strategy using observations including consumptions, inside temperatures and comfort measurements such as CO2 emissions obtained with sensors displayed in the building. The temperatures inside the building and the energy consumptions predicted by the calibrated model during a new time period are then compared to the corresponding observations. The model is then used to find a set of Pareto optimal schedulings and tunings of the building management system in terms of energy loads and thermal comfort using multi-objective optimization.

AB - This paper proposes a new methodology to reduce energy consumptions in large buildings while simultaneously optimizing thermal comfort. The model designed with an energy simulation program is calibrated by the Covariance Matrix Adaptation Evolutionary Strategy using observations including consumptions, inside temperatures and comfort measurements such as CO2 emissions obtained with sensors displayed in the building. The temperatures inside the building and the energy consumptions predicted by the calibrated model during a new time period are then compared to the corresponding observations. The model is then used to find a set of Pareto optimal schedulings and tunings of the building management system in terms of energy loads and thermal comfort using multi-objective optimization.

KW - Evolutionary algorithms

KW - Multi-objective optimization

KW - Pareto optimality

KW - Sustainable use of energy

KW - thermal comfort

U2 - 10.1109/DSW.2018.8439901

DO - 10.1109/DSW.2018.8439901

M3 - Conference contribution

AN - SCOPUS:85053141445

SN - 9781538644102

T3 - 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

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BT - 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Data Science Workshop, DSW 2018

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Le Corff S, Champagne A, Charbit M, Noziere G, Moulines E. OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK. Dans 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 41-45. 8439901. (2018 IEEE Data Science Workshop, DSW 2018 - Proceedings). doi: 10.1109/DSW.2018.8439901

OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK

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Une conférence

SDG des Nations Unies

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Contient cette citation