Comparison and Simulation of Building Thermal Models for Effective Energy Management

Abstract

Energy consumption reduction efforts in the residential buildings sector represent socio-economical, technological and environmental preoccupations which justify advanced scientific research. These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. Their principal goal is to provide supporting evidence of enhanced energy performances and predictions. More specifically, research questions are related to building thermal modeling which is the most appropriate in a smart grid context. In this context, the models are reviewed according to three categories. The first category is based on physical and basic principle modeling (white-box). The second offers a much simpler structure which is the statistical models (black-box). The black-box is used for prediction of energy consumption and heating/ cooling demands. Finally, the third category is a hybrid method (grey-box), which uses both physical and statistical modeling techniques. In this paper, we propose a detailed review and simulation of the main thermal building models. Our comparison and simulation results demonstrate that the grey-box is the most effective model for management of buildings energy consumption.

Application of building-dynamics-based control strategies to improve air-conditioning performance in educational buildings

“…Glass performance is dependent on many factors including the instantaneous location of the sun. A recent review (Kramer, van Schijndel, & Schellen, 2012) showed that the influence of the sun's radiation and thermal capacitance was consistently not taken into consideration in the models presented by researchers; particularly that a portion of the incident solar energy through a window will heat the interior air of the house while the remaining incident energy will be absorbed by the walls, floors, and furniture (Amara, Agbossou, Cardenas, Dubé, & Kelouwani, 2015).…”

Overview of the use of artificial neural networks for energy‐related applications in the building sector

“…1 The same proportion is observed in the United States with 40% of the greenhouse gases emissions and 70% of the electricity energy use imputable to the building sector. 2 For these reasons, the improvement of the existing building stock and the construction of efficient buildings is a major challenge to overcome in order to switch to a more sustainable world.…”

Reduced Order Modeling for Virtual Building Commissioning

“…A 2R2C model was used to describe dynamics associated with the internal air and mass. Similarly, Amara et al [21] used a 3R4C model to predict the conduction through the walls, disregarding their orientation. The long wave radiation was modeled using a single resistance, and the hot water heat exchanger, in the HVAC system, was modeled using a 2R1C network.…”

“…Another modeling approach is white-box modeling, which are modeling approaches developed through physics and first principles [10]. One of the best known white box modeling techniques for whole building energy simulation is EnergyPlus.…”

“…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.…”