###### Abstract
This work also illustrated the moisture buffer capacity of building materials in transient conditions: Numerical simulation for hygro-thermal performance and experimental data were compared showing good agreement [27]. [PERSON] et al. developed a software package for heating, HVAC, and moisture system analysis in buildings. The toolbox was constructed as a modular structure of standard building elements, using the graphical programming language, Simulink. To enable development of the toolbox, a common modeling platform was defined: A set of unique communication signals, material database, and documentation protocol [28]. [PERSON] et al. showed a proportional control system for residential buildings by setting up a dynamic simulation including the control system. An optimized MATLAB state-space method was used to model the heating system. Their results confirmed that proportional control was preferable to the two-position control for the thermal comfort, while there was not much difference in energy consumption between the two control systems [29]. [PERSON] et al. presented a dynamic model for evaluating energy consumption associated with ventilation. In addition, the model was used to study three ventilation scenarios of a typical home in Fairbanks--natural ventilation, a heat recovery ventilator (HRV), and an HRV with an additional particulate filter. The results highlighted a significant reduction of annual costs for energy [30]. [PERSON] et al. developed an innovative method for modeling multi-zone hydronic heating systems in buildings, again using the MATLAB/Simulink environment. A laboratory heating system was used for validation of the model. A series of trials were conducted in a multi-zone laboratory heating system located within the R&D division of a leading Italian boiler manufacturer [31]. [PERSON] et al. presented a Simulink model for simulating principles of a dynamic HVAC component model. The simulation result demonstrated that schedule-based temperature and damper position reset has a significant influence on energy conservation for summer and winter. Their simulation program can be particularly useful for dynamic analysis of different HVAC control strategies [32]. [PERSON] et al. developed a dynamic model of a building heating plant. The model, validated by means of experimental data collected by monitoring a real Italian building, showed very good agreement between measured and calculated data, and was used to assess different scenarios regarding the revamping of the heating plant [33]. [PERSON] et al. modeled the thermal behaviors of a multi-layer living wall. The aim of this study was to identify the most efficient wall configuration according to indoor and outdoor climate conditions by using a new simulation tool developed using MATLAB/Simulink. Furthermore, phase change materials were implemented in this research. The results showed an optimized active multi-layer living wall system could allow 27%-38% of reduced heating energy consumption while avoiding thermal discomfort [34]. [PERSON] generated a numerical model of a building with PCM to obtain the energy usage for heating and cooling of buildings. The model was validated by experimental measurements and a normative benchmark of a whole building. The author used the international building physics toolbox for her simulation and the results showed that the annual saving of total energy for heating and cooling was varied between 5% to 21% depending on the thermal comfort and the PCM position through the building [35]. Much of the research previously described used only solar effects or only internal radiation. Nevertheless, both these aspects have a significant effect on energy consumption. For this reason, models considering both lead to more accurate results.