Practical stochastic multivariable identification for buildings

Virk, G.S.; Cheung, J.Y.M.; Loveday, Dennis L.

doi:10.1016/0307-904x(95)00079-y

Cited by 9 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When operational data (measured sensor data) is unavailable and only thermo-physical characteristics are available, the parameters can be determined analytically [121,122], or by developing an analytical model as a reference model using available data and matching the resultant dynamics of the thermal network and reference models [74,80]. However, if there is good availability of measured data but a lack of thermophysical characteristics data, inverse methodologies [123][124][125] are applied to determine parameter values by minimizing the prediction error between the thermal-network model and the measured data [126][127][128][129]. We may categorize parametric identification methods into three groups based on the numerous methods provided in the literature:…”

Section: Parametric Identificationmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

View full text Add to dashboard Cite

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

show abstract

Section: Parametric Identificationmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

View full text Add to dashboard Cite

show abstract

“…Universal models can be either linear or non linear and take one or several inputs in consideration. Most of the time universal models were used for modelling systems or walls instead of the whole building [4], [5]. Extending these models for describing a model would require to fully know the different systems operating in the building but, most of the time, the description of buildings systems are unknown.…”

Section: Literature Reviewmentioning

confidence: 99%

ARX models inspired by physics as a service for building's users

Scanu¹,

Ploix²,

Bernaud³

et al. 2017

2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicati

View full text Add to dashboard Cite

show abstract

“…Description of Input/Output and exogenous variables the model are omitted since they result from classical arguments that can be found in existing references (Virk and Cheung [1995], Mustafaraja et al [2010], Gwerder and Tödtli [2009], Kolokotsa et al [2009]). …”

Section: Dynamic Model Of a Zonementioning

confidence: 99%

Unified NMPC for Multi-Variable Control in Smart Buildings

Lamoudi

Alamir

Béguery

2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

In this paper, the problem of minimizing energy consumption of a building zone under pre-assigned multi-variable comfort conditions and changing energy rate is addressed. The solution involves the use of a parameterized multi-variable Nonlinear Model Predictive Control (NMPC) that manages the actuation of heating/cooling, ventilation, lighting and blinds devices. Simulations of the resulting closed-loop in winter and summer seasons under varying rate profile are proposed to assess its efficiency. Moreover, a sensitivity analysis is conducted to show how the comfort level assignment impacts the level of energy consumption.

show abstract

Practical stochastic multivariable identification for buildings

Cited by 9 publications

References 3 publications

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

ARX models inspired by physics as a service for building's users

Unified NMPC for Multi-Variable Control in Smart Buildings

Contact Info

Product

Resources

About