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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	41-45
Number of pages	5
ISBN (Print)	9781538644102
DOIs	https://doi.org/10.1109/DSW.2018.8439901
Publication status	Published - 17 Aug 2018
Externally published	Yes
Event	2018 IEEE Data Science Workshop, DSW 2018 - Lausanne, Switzerland Duration: 4 Jun 2018 → 6 Jun 2018

Publication series

Name	2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Conference

Conference	2018 IEEE Data Science Workshop, DSW 2018
Country/Territory	Switzerland
City	Lausanne
Period	4/06/18 → 6/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Evolutionary algorithms
Multi-objective optimization
Pareto optimality
Sustainable use of energy
thermal comfort

Access to Document

10.1109/DSW.2018.8439901

Cite this

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. In 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings (pp. 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.",

pages = "41--45",

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. in 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings., 8439901, 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 41-45, 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Switzerland, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Champagne, Alain

AU - Charbit, Maurice

AU - Noziere, Gilles

AU - Moulines, Eric

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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

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DO - 10.1109/DSW.2018.8439901

M3 - Conference contribution

AN - SCOPUS:85053141445

SN - 9781538644102

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

SP - 41

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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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ER -

Le Corff S, Champagne A, Charbit M, Noziere G, Moulines E. OPTIMIZING THERMAL COMFORT and ENERGY CONSUMPTION in A LARGE BUILDING WITHOUT RENOVATION WORK. In 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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