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

Coninck, Roel De; Magnusson, Fredrik; Åkesson, Johan; Helsen, Lieve

doi:10.1080/19401493.2015.1046933

Cited by 91 publications

(66 citation statements)

References 21 publications

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“…This problem has been investigated by different authors who used relatively similar optimization strategies but different model structures. De Coninck et al (2015) and Rehab & André (2015) used second order thermal models but with different structure and inputs. Himpe & Janssens (2015) used a model with four states which was correlated with the HVAC system and the solar radiations.…”

Section: Model Comparisonmentioning

confidence: 99%

An efficient Bayesian experimental calibration of dynamic thermal models

Raillon

Ghiaus

2018

Energy

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Experimental calibration of dynamic thermal models is required for model predictive control and characterization of building energy performance. In these applications, the uncertainty assessment of the parameter estimates is decisive; this is why a Bayesian calibration procedure (selection, calibration and validation) is presented. The calibration is based on an improved Metropolis-Hastings algorithm suitable for linear and Gaussian state-space models. The procedure, illustrated on a real house experiment, shows that the algorithm is more robust to initial conditions than a maximum likelihood optimization with a quasi-Newton algorithm. Furthermore, when the data are not informative enough, the use of prior distributions helps to regularize the problem.

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Section: Model Comparisonmentioning

confidence: 99%

An efficient Bayesian experimental calibration of dynamic thermal models

Raillon

Ghiaus

2018

Energy

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“…The reduced order model (ROM) showed good results for simulations compared to detailed models in which the energy use for heating, the heat emitted by the heating system and the air temperature was obtained. De Coninck et al [13] developed a toolbox for data-driven grey box models. This toolbox automates different steps in the system identification process.…”

Section: Introductionmentioning

confidence: 99%

Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

2019

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In school and office buildings the ventilation system has a large contribution to the total energy use. A control strategy that adjusts the operation to the actual demand can significantly reduce the energy use. This is important in rooms with a highly fluctuating occupancy profile. However, a standard rule-based control is reactive, making the installation ‘lag behind’ in relation to the demand. A model predictive control (MPC) might be a solution. To implement an MPC control first a suitable model must be identified for reliable predictions of room temperature and CO2 concentration. For CO2 predictions three scenarios are proposed respectively counting camera, lecture schedule and motion sensor. Two model identification techniques are evaluated: ARX and RC models. For identifying the heating dynamics of the case study building the 3 state RC model showed a good performance, a 5 step ahead prediction on a 15 minute time interval indicated a RMSE of approximately 0.60 °C. The 3rd order ARX model indicated similar results, however the cross validation demonstrated that the RC model outperforms the ARX model. For CO2 predictions the counting scenario resulted in the most accurate n-step ahead predictions. The RMSE found for the RC model is at maximum 90 ppm while 140 ppm for the ARX model. RC models are recommended for modelling all-air HVAC systems attributed by the higher prediction accuracy over ARX models. In addition, these models still contain physical parameters compared to ARX models.

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

Cited by 91 publications

References 21 publications

An efficient Bayesian experimental calibration of dynamic thermal models

An efficient Bayesian experimental calibration of dynamic thermal models

Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building

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

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