Development and validation of a gray box model to predict thermal behavior of occupied office buildings

Berthou, Thomas; Stabat, Pascal; Salvazet, Raphael; Marchio, Dominique

doi:10.1016/j.enbuild.2014.01.038

Cited by 123 publications

(105 citation statements)

References 15 publications

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“…Solar gains are easily accessible in building energy simulation tools such as TRNSYS, but difficult to obtain in a real building. Different methodologies can be found in literature in order to include direct and global solar radiation in this kind of models [22,30].…”

Section: Solar Irradiation Effective Areamentioning

confidence: 99%

Methodology for evaluating the energy renovation effects on the thermal performance of social housing buildings: Monitoring study and grey box model development

Terés-Zubiaga

Escudero-Revilla

García-Gáfaro

et al. 2015

Energy and Buildings

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Section: Solar Irradiation Effective Areamentioning

confidence: 99%

Methodology for evaluating the energy renovation effects on the thermal performance of social housing buildings: Monitoring study and grey box model development

Terés-Zubiaga

Escudero-Revilla

García-Gáfaro

et al. 2015

Energy and Buildings

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“…Initialization of parameters was calculated by applying the French thermal code (RT 2005-2012), see Appendix A [11,19,20]. Table 1 presents the initial values of the parameters defining buildings' characteristics.…”

Section: Parameters Estimationmentioning

confidence: 99%

“…The grey-box models provide some advantages in the buildings' thermal modeling process, in particular, ease of their use and the possibility to link their parameters to global buildings' physical characteristics, such as the heat resistance and the mass capacity. These models can be used for different purposes such as control of the indoor environment [8,9], forecasting energy consumption, and evaluating buildings' energy performance [10][11][12]. However, their practical use is subjected to the difficulty of the determination of their optimal order.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

et al. 2019

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The use of grey-box models for short-time forecasting of buildings' thermal behavior requires the determination of the models' order since this order could influence the grey-box models' performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models' order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models' order depends on the buildings' thermal conditions, but generally lies between two and three with an error less than 0.2 • C and a fit percent greater than 90%.

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“…A large number of papers aiming at selecting a proper model structure and an identification algorithm can be found in the literature of building science. Berthou et al [11] and Bacher and Madsen [12] performed comparative studies of various thermal network model structures. Similar studies to find proper model orders for building systems can also be found in [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

Kim

Cai

Ariyur

et al. 2016

Building and Environment

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Identification approaches applied to semi-physical thermal network structures, so called gray-box modeling approaches, are popular in building science for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. However conventional identification approaches applied to thermal networks fail when there are significant unmeasured heat gains that influence building responses. This paper presents a method to obtain improved gray-box building models from closed loop data having significant unmeasured disturbances. The method estimates both physical parameters of a building thermal network model and also a disturbance model that characterizes the unmeasured inputs. The performance of the algorithm is demonstrated using numerical and experimental results.

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Development and validation of a gray box model to predict thermal behavior of occupied office buildings

Cited by 123 publications

References 15 publications

Methodology for evaluating the energy renovation effects on the thermal performance of social housing buildings: Monitoring study and grey box model development

Methodology for evaluating the energy renovation effects on the thermal performance of social housing buildings: Monitoring study and grey box model development

Determination of the Optimal Order of Grey-Box Models for Short-Time Prediction of Buildings’ Thermal Behavior

System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach

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