Toolbox for development and validation of grey-box building models for forecasting and control
(2016) In Journal of Building Performance Simulation, Taylor & Francis 9(3). p.288-303- Abstract
- As automatic sensing and information and communication technology get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and Air-Conditioning models and the optimization framework in JModelica.org. The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection, parameter estimation and validation. To validate the methodology, different grey-box models are... (More)
- As automatic sensing and information and communication technology get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and Air-Conditioning models and the optimization framework in JModelica.org. The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection, parameter estimation and validation. To validate the methodology, different grey-box models are identified for a single-family dwelling with detailed monitoring data from two experiments. Validated models for forecasting and control can be identified. However, in one experiment the model performance is reduced, likely due to a poor information content in the identification data set. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7366117
- author
- De Coninck, Roel ; Magnusson, Fredrik LU ; Åkesson, Johan and Helsen, Lieve
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Building Performance Simulation, Taylor & Francis
- volume
- 9
- issue
- 3
- pages
- 288 - 303
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:84959112290
- wos:000374992500004
- ISSN
- 1940-1507
- DOI
- 10.1080/19401493.2015.1046933
- project
- Numerical and Symbolic Algorithms for Dynamic Optimization
- LCCC
- language
- English
- LU publication?
- yes
- id
- f4995b32-00e2-4c69-a890-30848a0402e7 (old id 7366117)
- date added to LUP
- 2016-04-01 11:07:34
- date last changed
- 2022-05-13 23:09:00
@article{f4995b32-00e2-4c69-a890-30848a0402e7,
abstract = {{As automatic sensing and information and communication technology get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and Air-Conditioning models and the optimization framework in JModelica.org. The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection, parameter estimation and validation. To validate the methodology, different grey-box models are identified for a single-family dwelling with detailed monitoring data from two experiments. Validated models for forecasting and control can be identified. However, in one experiment the model performance is reduced, likely due to a poor information content in the identification data set.}},
author = {{De Coninck, Roel and Magnusson, Fredrik and Åkesson, Johan and Helsen, Lieve}},
issn = {{1940-1507}},
language = {{eng}},
number = {{3}},
pages = {{288--303}},
publisher = {{Taylor & Francis}},
series = {{Journal of Building Performance Simulation, Taylor & Francis}},
title = {{Toolbox for development and validation of grey-box building models for forecasting and control}},
url = {{https://lup.lub.lu.se/search/files/2399347/7985374.pdf}},
doi = {{10.1080/19401493.2015.1046933}},
volume = {{9}},
year = {{2016}},
}