2024Journal Article ER5 Auteurs : Bitar, Rina; Youssef, Nicolas; Chamoin, Julien; Chehade, Fadi Hage; Defer, Didier Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control-Application to a Residential Building In: Buildings, 2024, (ACL). Links @article{bitar:hal-04734040,
title = {Simultaneous Energy Optimization of Heating Systems by Multi-Zone Predictive Control-Application to a Residential Building},
author = {Rina Bitar and Nicolas Youssef and Julien Chamoin and Fadi Hage Chehade and Didier Defer},
url = {https://hal.science/hal-04734040},
doi = {10.3390/buildings14103241},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
journal = {Buildings},
publisher = {MDPI},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
|
2022Journal Article ER5 Auteurs : Abdellatif, Makram; Chamoin, Julien; Nianga, Jean-Marie; Defer, Didier A thermal control methodology based on a machine learning forecasting model for indoor heating In: Energy and Buildings, vol. 255, pp. 111692, 2022, ISSN: 0378-7788, (ACL). Abstract | Links @article{ABDELLATIF2022111692,
title = {A thermal control methodology based on a machine learning forecasting model for indoor heating},
author = {Makram Abdellatif and Julien Chamoin and Jean-Marie Nianga and Didier Defer},
url = {https://www.sciencedirect.com/science/article/pii/S0378778821009762},
doi = {https://doi.org/10.1016/j.enbuild.2021.111692},
issn = {0378-7788},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Energy and Buildings},
volume = {255},
pages = {111692},
abstract = {To take advantage of the data generated in buildings, this document proposes a methodology based on a machine learning model to improve thermal comfort and energy efficiency. This methodology uses measured data (e.g., indoor/outdoor temperature, relative humidity, etc.) and forecast data (e.g., meteorological data) to train a multiple linear regression model to forecast the indoor temperature of the space under study. Using the genetic algorithm optimization method, this model is then used to evaluate the different heating strategies generated. For each strategy, a score is assigned according to user-defined criteria in order to prioritize them and select the best one. By studying an office building simulated under the TRNSYS software, a multiple linear regression model was implemented with errors less than 1% and an adjusted R2 coefficient close to 0.9. Compared to a conventional heating strategy, this methodology can improve thermal comfort by up to 43%.},
note = {ACL},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
To take advantage of the data generated in buildings, this document proposes a methodology based on a machine learning model to improve thermal comfort and energy efficiency. This methodology uses measured data (e.g., indoor/outdoor temperature, relative humidity, etc.) and forecast data (e.g., meteorological data) to train a multiple linear regression model to forecast the indoor temperature of the space under study. Using the genetic algorithm optimization method, this model is then used to evaluate the different heating strategies generated. For each strategy, a score is assigned according to user-defined criteria in order to prioritize them and select the best one. By studying an office building simulated under the TRNSYS software, a multiple linear regression model was implemented with errors less than 1% and an adjusted R2 coefficient close to 0.9. Compared to a conventional heating strategy, this methodology can improve thermal comfort by up to 43%. |
2020Journal Article ER5 Auteurs : Abdellatif, Makram; Chamoin, Julien; NIANGA, Jean-Marie; Defer, Didier Prédiction par régression linéaire multiple : application au comportement thermique d’un bâtiment In: Academic Journal of Civil Engineering, vol. Vol 38 No 1, pp. Special Issue-RUGC 2020 Marrakech, 2020, (ACTI). Links @article{ABDELLATIF2020,
title = {Prédiction par régression linéaire multiple : application au comportement thermique d’un bâtiment},
author = {Makram Abdellatif and Julien Chamoin and Jean-Marie NIANGA and Didier Defer},
doi = {10.26168/AJCE.38.1.19},
year = {2020},
date = {2020-01-01},
journal = {Academic Journal of Civil Engineering},
volume = {Vol 38 No 1},
pages = {Special Issue-RUGC 2020 Marrakech},
publisher = {Academic Journal of Civil Engineering},
note = {ACTI},
keywords = {ER5},
pubstate = {published},
tppubtype = {article}
}
|
2020Conference ER5 Auteurs : Abdellatif, Makram; Chamoin, Julien; Defer, Didier; NIANGA, Jean Marie Prédiction par régression linéaire multiple : application au comportement thermique d'un bâtiment Rencontre Universitaire de Genie Civil, Marrakech, Morocco, 2020. Links @conference{Abdellatif2020b,
title = {Prédiction par régression linéaire multiple : application au comportement thermique d'un bâtiment},
author = {Makram Abdellatif and Julien Chamoin and Didier Defer and Jean Marie NIANGA},
url = {https://hal.archives-ouvertes.fr/hal-03211526},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {Rencontre Universitaire de Genie Civil},
address = {Marrakech, Morocco},
keywords = {ER5},
pubstate = {published},
tppubtype = {conference}
}
|
2019Proceedings Article ER5 Auteurs : Abdellatif, Makram; Chamoin, Julien; Defer, Didier A thermal control methodology based on a predictive model for indoor heating management In: pp. 01001, 2019. Links @inproceedings{refId0c,
title = {A thermal control methodology based on a predictive model for indoor heating management},
author = {Makram Abdellatif and Julien Chamoin and Didier Defer},
url = {https://doi.org/10.1051/matecconf/201929501001},
doi = {10.1051/matecconf/201929501001},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {MATEC Web Conf.},
volume = {295},
pages = {01001},
keywords = {ER5},
pubstate = {published},
tppubtype = {inproceedings}
}
|
