An Iterative Methodology for Model Complexity Reduction in Residential Building Simulation

Abstract

The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building.

On the assessment and control optimisation of demand response programs in residential buildings

“…Despite the extensive research carried out over the last decades, the identification of proper methodologies capable of detecting a trade-o↵ between accuracy and computational cost, which depends on the specific application the model is intended for, is still an open challenge. As for instance, De Rosa et al [107] introduced a top-down methodology to detect a reduced-order model capable of simulating the building energy consumption in a shortterm horizon, compatible with the implementation of demand response measures, and with a reasonable computational cost. The methodology is based on the progressively reduction of the complexity of building models, calibrated against experimental data, while retaining the model structure.…”

“…5, which provides the best thermal response of the system, as for instance, measured by the room temperature T r,0 . Calibration procedures correlate the variation in thermal performance (i.e., T r ) with the variation in parameters ( p ), which can be inferred from the semi-physical modelling [113].…”

Modeling of Heating and Cooling Energy Needs in Different Types of Smart Buildings

“…In the literature, physical-based techniques mainly use numerical models based on finite element, finite difference and finite volume approaches for the simulation of thermal energy demand in various buildings [16]- [23]. In [16], the author developed a finite-difference method to improve the conduction heat transfer through walls when implementing it in TRNSYS.…”

“…Hourly values for energy demand and indoor temperature are computed with respect to hourly weather data. Authors in [23] analyze different levels of model complexity, from commercial building energy simulation tools to low order calibrated thermal network models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model.…”

Report on a Plus-Energy District with Low-Temperature DHC Network, Novel Agrothermal Heat Source, and Applied Demand Response

“…All the individual parts are described further below. A more detailed description and assessment of the model is also given in [36].…”