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Toolbox for development and validation of grey-box building models for forecasting and control

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Author

  • Roel De Coninck
  • Fredrik Magnusson
  • Johan Åkesson
  • Lieve Helsen

Summary, in English

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.

Publishing year

2016

Language

English

Pages

288-303

Publication/Series

Journal of Building Performance Simulation, Taylor & Francis

Volume

9

Issue

3

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Control Engineering

Status

Published

Project

  • Numerical and Symbolic Algorithms for Dynamic Optimization
  • LCCC

Research group

  • LCCC

ISBN/ISSN/Other

  • ISSN: 1940-1507