Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency.

It is challenging to apply heat flow through a thermal bridge, which requires the analysis of 2D or 3D heat transfer to building energy simulation (BES). Research on the dynamic analysis of thermal bridges has been underway for many years, but their utilization remains low in BESs. This paper proposes a thermal bridge modeling and a dynamic analysis method that can be easily applied to BESs. The main idea begins with an analogy of the steady-state analysis of thermal bridges. As with steady-state analysis, the proposed method first divides the thermal bridge into a clear wall, where the heat flow is uniform, and the sections that are not the clear wall (the thermal bridge part). For the clear wall part, the method used in existing BESs is applied and analyzed. The thermal bridge part (TB part) is modeled with the linear time-invariant system (LTI system) and the system identification process is performed to find the transfer function. Then, the heat flow is obtained via a linear combination of the two parts. This method is validated by comparing the step, sinusoidal and annual outdoor temperature response of the finite differential method (FDM) simulation. When the thermal bridge was modeled as a third-order model, the root mean square error (RMSE) of annual heat flow with the FDM solution of heat flow through the entire wall was about 0.1 W.

Ventilation or filtration control is widely applied to improve indoor particle matter (PM) concentration. Adjusting the ventilation rates to control indoor PM levels can affect the concentration of other indoor pollutants and energy costs, and increasing the filtration flow rate can lower the indoor PM concentration, but also increase the fan energy consumption. In this study, we developed a ventilation and filtration control strategy to determine the optimal control mode and flow rate of the system to meet indoor PM (especially PM2.5) concentration, ensure adequate indoor air quality (IAQ), and minimize fan energy consumption. First, a dynamic model to estimate the indoor PM2.5 generation rate was developed based on the mass balance model and then verified by experiments. Next, the control limit (CL) curve was developed on the basis of the indoor PM2.5 characteristics depending on ventilation and filtration control during various indoor and outdoor PM2.5 conditions (indoor PM2.5 generation rate and outdoor PM2.5 concentration). In addition, an algorithm was proposed to determine the optimal control mode and flow rate of the system. Condition zone control can keep indoor PM2.5 below or as close to the desired target concentration as possible, maintain IAQ within acceptable ranges, and save about 15~70% of fan energy compared with the conventional rule-based control under the case condition.

The objective of this study was to investigate the effect of children’s daily activity patterns on the size-resolved indoor particles in a pre-school classroom, qualitatively and quantitatively. Real-time particle number size distribution data in conjunction with activity were collected experimentally in a pre-school classroom during two seasons: late spring and early winter. We divided daily activities into six groups: Arrival, Large Group, Small Group, Naptime, Dismissal and Unoccupied. The concentration variation forms of coarse particles were compared qualitatively among activity groups. The results indicated that noteworthy coarse particle concentration variations were observed during each activity group. These variations were caused by four factors: presence/absence of children, variation in the number of children, activity intensity and presence of sources. The emission rates were estimated to quantify the impact of activities. Time series emission rates were deduced from the piecewise-constant inputs and outputs model. The piecewise-constant emission rates were averaged for sequential parts to reduce noise and ensure daily repeated peaks were not missed. The sequential part-constant emission rates were consistent with previously described particle number concentration variation results and the predicted indoor particle number concentration data with sequential part-constant emission rates agreed well with measured data.

It is challenging to apply heat flow through a thermal bridge, which requires the analysis of 2D or 3D heat transfer to building energy simulation(BES). Research on the dynamic analysis of thermal bridges has been underway for many years, but their utilization remains low in BESs. This paper proposes a thermal bridge modeling and a dynamic analysis method that can be easily applied to BESs. The main idea begins with an analogy of the steady-state analysis of thermal bridges. As with steady-state analysis, the proposed method first divides the thermal bridge into a clear wall, where the heat flow is uniform, and the sections that are not the clear wall (the thermal bridge part). For the clear wall part, the method used in existing BESs is applied and analyzed. The thermal bridge part (TB part) is modeled with the linear time-invariant system (LTI system) and the system identification process is performed to find the transfer function. Then, the heat flow is obtained via a linear combination of the two parts. This method is validated by comparing the step, sinusoidal and annual outdoor temperature response of the finite differential method(FDM) simulation. When the thermal bridge was modeled as a third-order model, the root mean square error(RMSE) of annual heat flow with the FDM solution of heat flow through the entire wall was about 0.1W.

It is not easy to dynamically analyze thermal bridges that require multi-dimensional analysis in building energy simulations, which are mostly one-dimensional platforms. To solve this problem, many studies have been conducted and, recently, a study was conducted to model a thermal bridge based on the data obtained by approaching this in a similar way to steady-state analysis, showing high accuracy. This was an early-stage study, which is only applicable when the indoor temperature is constant. By extending the study, a thermal bridge model that can be applied even when the indoor temperature changes over time in building energy simulations is proposed and validated. Since the governing equation, the heat diffusion equation, is linear, the key idea is to create and apply two thermal bridge transfer function models by expressing the heat flow that enters the room as a linear combination of the transfer function for indoor temperature and the transfer function for outdoor temperature. For the proposed thermal bridge model, the NRMSE of the model itself showed a high accuracy of 0.001, and in the verification through annual simulation using the model, the NRMSE showed an accuracy of 0.1.