Influence of data pre-processing and sensor dynamics on grey-box models for space-heating: Analysis using field measurements

Yu, Xingji; Skeie, Kristian; Knudsen, Michael Dahl; Ren, Zhengru; Imsland, Lars; Georges, Laurent

doi:10.1016/j.buildenv.2022.108832

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…𝜙 ℎ and Sg are the energy flux (in W) from the heating system and the solar radiation respectively. A two states (also called second-order) model is generally regarded as a sufficient model for modeling building thermal dynamics, in most of studies [24][25][26][27][28][29]. One state (first-order) grey-box model might not be sufficient to 'catch' the key dynamics [13], and few cases requires three states model or above.…”

Section: Grey-box Modelling Of Building Thermal Dynamicsmentioning

confidence: 99%

Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches

Zhang

Rasmussen

Sælens

et al. 2022

Renewable and Sustainable Energy Reviews

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Section: Grey-box Modelling Of Building Thermal Dynamicsmentioning

confidence: 99%

Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches

Zhang

Rasmussen

Sælens

et al. 2022

Renewable and Sustainable Energy Reviews

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“…A considerable amount of research has already been done to find suitable mono-zone grey-box model structures to be applied to MPC of buildings (Bacher & Madsen, 2011;Berthou et al, 2014;Harb et al, 2016;Reynders et al, 2014;Viot et al, 2018). In the previous study (Yu et al, 2022), a 3R2C grey-box model has proven to be a suitable trade-off between model complexity and accuracy for the test case. Therefore, this model structure is used for the MPC controller in our work.…”

Section: Methodology Grey-box Modelmentioning

confidence: 99%

Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings

Georges

Imsland

2022

E3S Web Conf.

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Model predictive control (MPC) is an advanced optimal control technique to minimize a control objective while satisfying a set of constraints and is well suited to activate the building energy flexibility. The MPC controller performance depends on the accuracy of the model prediction. Inaccurate predictions can directly lead to low control performance. Linear time-invariant (LTI) models are often used in MPC in buildings. However, LTI models do not adapt to the weather conditions varying throughout the whole space-heating season, which makes the MPC based on LTI models not perform well over a long period of time. Therefore, this study introduces an adaptive MPC where the parameters of a linear grey-box model are continuously updated in real-time. Two alternative versions of this adaptive control are investigated. The first one, called partially adaptive MPC, only updates the effective window area of the grey-box model, while the second one, called fully adaptive MPC, updates all the parameters of the grey-box model. Results show that the partially adaptive MPC is not able to deliver satisfactory prediction performance. The fully adaptive MPC shows better performance compared to the other models when implemented in a MPC, especially in avoiding thermal comfort violation.

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“…The methodology builds upon a grey-box modeling (GBM) approach that includes the thermal zone and space heating system. While many researchers have used GBMs to estimate the characteristics of the thermal zone [2,3,4], mostly driven by the desire to develop predictive control and balance loads [5], fewer have used GBMs to detect faults and sub-optimal efficiency of space heating system [6,7].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Heating System Performance in Office Buildings through Grey-box Modeling

Fjerbæk,

Hviid

2023

J. Phys.: Conf. Ser.

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The transition to renewable energy sources requires that larger shares of heating production should come from heat pumps both on individual level and in district heating networks. The efficiency of heat pumps is highly dependent on the temperature lift. Therefore, it is key to assess the possibilities of low-temperature heating in buildings. This paper proposes a data-driven methodology to analyse and benchmark the performance of radiator heating systems, by estimating parameters for the building’s envelope and heating system using Modelica with ModestPy. The methodology requires little a-priori knowledge and common data sources and provide valuable insights on the potentials of lowered operating temperatures. The methodology was tested on a newly renovated office building in Copenhagen and was able to consistently estimate characteristics of the envelope and capacity of the heating system. By using information on the actual capacity of the heating system, the methodology demonstrated the potential for lowering the heating supply temperatures, thereby reducing heat pump electricity consumption with 9 %.

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Influence of data pre-processing and sensor dynamics on grey-box models for space-heating: Analysis using field measurements

Cited by 7 publications

References 37 publications

Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches

Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches

Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings

Benchmarking Heating System Performance in Office Buildings through Grey-box Modeling

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