Reduced order modeling and parameter identification of a building energy system model through an optimization routine

Harish, V. S. K. V.; Kumar, Arun

doi:10.1016/j.apenergy.2015.10.137

Cited by 84 publications

(38 citation statements)

References 31 publications

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“…A number of studies have deployed the purely physical models such as EnergyPlus to achieve reducedorder RC (resistance-capacitance) models for simulating thermal dynamics and DR performance in commercial buildings [17,38,39,40,41,42,43]. Fast power demand response strategies that involve HVAC systems in commercial buildings were evaluated based on the RC model [44,45], which demonstrate that building HVAC systems are quite suitable for demand response.…”

Section: Current Approaches To Quantifying Building Dr Potentialmentioning

confidence: 99%

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

et al. 2016

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This paper presents a novel demand response estimation framework for residential and commercial buildings using a combination of EnergyPlus and two-state models for thermostatically controlled loads. Specifically, EnergyPlus models for commercial and multi-dwelling residential units are applied to construct exhaustive datasets (i.e., with more than 300M data points) that capture the detailed load response and complex thermodynamics of several building types. Subsequently, regression models are fit to each dataset to predict DR potential based on key inputs, including hour of day, set point change and outside air temperature. For single residential units, and residential thermostatically controlled loads (i.e. water heaters and refrigerators) a two-state model from the literature is applied. The proposed framework is then validated for commercial buildings through a comparison with a dataset composed of 11 buildings during 12 demand response events. In addition, the use of the proposed simplified DR estimation framework is presented in terms of two cases (1) peak load shed prediction in an individual building and (2) aggregated DR up/down capacity from a large-scale group of different buildings.

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Section: Current Approaches To Quantifying Building Dr Potentialmentioning

confidence: 99%

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

et al. 2016

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“…The authors all showed that this simplified approach could mimic the results (typically expressed as operative temperatures) of the more complex models obtained with BES software within an error margin of ± 0.5-1 K. By applying model order reduction methods, the complexity of the obtained linear model can be further reduced [23,21,20,24]. Both for the grey-box and for the white-box approach, the necessary level of model complexity in order to obtain a good MPC remains unknown and no systematic method to determine this optimal model complexity is available [5,25]. Some studies have investigated the influence of the model order on the model off-line prediction performance [26].…”

Section: Introductionmentioning

confidence: 99%

Impact of the controller model complexity on model predictive control performance for buildings

Picard

Drgoňa

Kvasnica

et al. 2017

Energy and Buildings

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Model predictive control (MPC) for heating, ventilation and air conditioning (HVAC) in buildings requires accurate controller models of the building envelope and its HVAC systems. Controller models are typically obtained by means of black-or grey-box system identification or using a white-box modelling approach. However, the necessary level of model complexity used by each method in order to obtain good MPC performance remains a priori unknown and no systematic method or examples showing the optimal complexity is available. This paper systematically investigates the required controller model complexity necessary to obtain optimal control performance for a given building. First, a 6-room house is modeled in detail using building energy simulation software. The building model is then linearised to obtain a linear time invariant (LTI) state-space model (SSM) and the upper bound of the control performance is computed using an MPC with the SSM both as controller and as plant model. The accuracy of the SSM (containing more than 250 states) is then artificially decreased by reducing its number of states to different orders ranging from 4 to 100 using balanced truncation model order reduction technique. The performances of MPCs using these controller models are then compared with the upper bound for both a standard MPC formulation (S-MPC) and an offset-free formulation (OSF-MPC) and with the performance of a rule-based-controller (RBC). The procedure is repeated for the same house model with a higher level of insulation and for a lighter weight construction. This paper shows that the controller model should contain a minimum of states to model each zone separately, and that the walls and floors separating the zones should also have enough states to act as a low pass filter with correct cut-off frequency. The minimum number of states further increases with the building mass content. In the case of the investigated 6-room house, the thermal comfort achieved by MPC using a controller model with a minimum of 30 states instead of 20 states was improved with a factor 2 to 6 without significant increase of the energy use, showing that good MPC performances require controller models with a significantly higher number of states than the order used by most of the black-and grey-box system identification techniques. The minimum required number of states might be chosen lower when OSF-MPC is used instead of conventional MPC. However, OSF-MPC might significantly increase the energy use when poor controller models (high model mismatch) are used. Furthermore, if the controller model is an LTI model, this paper shows that the CPU time necessary to solve the MPC optimization problem becomes independent of the number of states of the controller model when a dense approach is used. The controller model can thus be as complex as necessary to produce accurate predictions without increasing the computation time of the optimization.

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“…Black-box modeling is a data driven modeling approach which uses time series data to statistically fit a model to determine building parameters. Black-box modeling does not provide any information about the behavioral mechanism of the building [9], it solely is a statistical representation of building data correlations. Black box models only focus on finding relationships between the model's inputs and outputs [10], which is useful for predicting building performance given a specific outdoor condition, but not especially for virtual commissioning.…”

Section: Figure 1 Processes Of Energy Transfer In a Conditioned Spacmentioning

confidence: 99%

“…Grey-box modeling is still built on the foundation of first principles, but in conjunction, it also uses parameter optimization with actual operational data [9]. For thermal systems, grey-box modeling uses sets of differential equations to model the dynamics of heat transfer and thermal storage.…”

Section: Figure 1 Processes Of Energy Transfer In a Conditioned Spacmentioning

confidence: 99%

“…The building could be modeled using a high order thermal circuit, but that does little to simplify the energy modeling process. ROMs are typically excited through either an impulse, ramp, or step input [9], and the response is analyzed to estimate the system's parameters, which was used to determine the order of the system. EnergyPlus was used to simulate a step input for the BESTEST case, which was accomplished by modifying the model's weather file and HVAC controls.…”

Section: Structure Of the Case Study Reduced Order Modelmentioning

confidence: 99%

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Reduced Order Modeling for Virtual Building Commissioning

Rosin¹

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Reduced order modeling and parameter identification of a building energy system model through an optimization routine

Cited by 84 publications

References 31 publications

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

Impact of the controller model complexity on model predictive control performance for buildings

Reduced Order Modeling for Virtual Building Commissioning

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