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

Författare

  • 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.

Publiceringsår

2016

Språk

Engelska

Sidor

288-303

Publikation/Tidskrift/Serie

Journal of Building Performance Simulation, Taylor & Francis

Volym

9

Issue

3

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Control Engineering

Status

Published

Projekt

  • Numerical and Symbolic Algorithms for Dynamic Optimization
  • LCCC

Forskningsgrupp

  • LCCC

ISBN/ISSN/Övrigt

  • ISSN: 1940-1507