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Pressure differences in the envelope of a building result in leakage airflow (i.e., the unintended flow of air). This can lead to increased building heating and cooling energy, decreased thermal comfort for occupants, and the spread of moisture. To address this problem, it is necessary to know the leakage airflow in a building. Generally, the leakage airflow in a building is calculated by determining the leakage function through fan pressurization methods, such as the blower door test, and substituting the pressure difference measured by the pressure sensor. However, it is difficult to install continuous pressure sensors in an operating building. Therefore, this study proposes a method to utilize the supply and return airflow of an air conditioning system to predict the variation in the leakage airflow with changing indoor and outdoor airflow, and the efficacy of this approach was verified through experiments. The experiment measured the indoor and outdoor pressure difference of the building with a change in the speed of the supply and return fans and the opening rate of the variable air volume (VAV) damper. As a result of the experiment, the indoor–outdoor pressure difference is proportional to the difference between the indoor supply airflow and the ventilation airflow. In addition, the relationship between the pressure difference and the leakage airflow was derived through the pressurization/decompression method using an air handler, and the leakage airflow from the pressure difference generated by the operation of the air conditioning system was calculated. Lastly, the relationship between the supply and return airflow difference and the leakage airflow was derived based on the experimental results, and the leakage airflow was predicted based on the relationship. [ABSTRACT FROM AUTHOR]

The economizer system is a method of improving energy efficiency through the operating method, which introduces outdoor air through dampers when the outdoor air temperature or enthalpy is lower than the that of the indoor air. The set values used for economizer control include the mixed air temperature and high and low limits. The set values are presented as fixed values in the relevant standards and are controlled to be fixed during actual operation, which may lead to issues such as indoor discomfort, poor indoor air quality, and energy wastage. Therefore, it is necessary to optimize economizer control by determining appropriate set values considering the indoor and outdoor environments. To this end, this paper reviewed the economizer system, control method, control set values, and prediction models in buildings. As a result, it was concluded that optimal economizer control is possible by utilizing a prediction model. [ABSTRACT FROM AUTHOR]

The pressure difference in buildings causes indoor and outdoor airflow, significantly impacting the indoor thermal environment and building energy consumption due to the introduction of outdoor air. The pressure difference in buildings is highly variable, influenced by outdoor wind speed, indoor–outdoor temperature differences, and heating, ventilating and air conditioning (HVAC) system operation, making it difficult to consider this factor during general HVAC system operation, potentially leading to an imbalance in indoor and outdoor pressure differences. Therefore, this study proposes an appropriate operation strategy for HVAC system return fans considering indoor–outdoor pressure differences. The proposed strategy involves adjusting the return fan airflow to maintain a constant indoor airflow balance, thereby controlling the indoor–outdoor pressure difference, satisfying the indoor thermal environment, and reducing HVAC system energy consumption. To evaluate the proposed strategy, dynamic simulations using TRNSYS and TRNFLOW were utilized, targeting one floor of an office building equipped with a variable air volume (VAV) system. The evaluation results showed that the maximum pressure difference decreased from −142 Pa to −18 Pa compared to the existing strategy, and the total energy consumption of the HVAC system was reduced by 29%, highlighting the importance of considering pressure differences during HVAC system operation. [ABSTRACT FROM AUTHOR]

Energy efficiency of the HVAC system can be improved through system renovation and operating method improvement. Economizer control, one of the energy efficient measures through improvement of operating method, introduces outdoor air when outdoor air is sufficient for cooling. There are high/low limit that determine the range of economizer control and mixed air temperature as control set-points. Economizer is controlled with the user's or manager's experience, and the set-point is operated fixed. This causes problems energy waste because it does not consider indoor and outdoor environments. Therefore, the purpose of this study is to develop an optimal economizer control of VAV system that resetting the set-point considering the indoor and outdoor environments. To this, a machine learning model was used to develop a model that predicts the future state based on the current state. Based on the developed prediction model, the optimal economizer control of VAV system that resets the mixed air temperature set-point in real time was developed and the control method was evaluated through simulation. As a result, it was confirmed that the mixed air temperature set-point changed in real time, and that about 20% of energy consumption was saved compared to the existing dry- bulb temperature control.

The purpose of this study is to propose a control method that optimizes the set point of the variable air volume (VAV) terminal unit using real-time prediction model of indoor thermal and air environment and energy consumption. Indoor thermal environment and air environment are predicted through indoor load and carbon dioxide (CO2) concentration. A prediction model was developed through operation data of the VAV terminal unit by artificial neural network (ANN) algorithm. The developed prediction model was used for real-time set point control of the VAV terminal unit. The optimal control of VAV terminal unit can be expressed in 3 step. First, it predicts the current indoor load and CO2 concentration. Second, all supply temperatures and supply air flow rate that can be provided in the predicted condition are repeatedly simulated. Finally, the set point of the minimum energy consumption is applied to the control. The evaluation of the proposed control method was compared with the dual maximum control logic. Comparative evaluation was performed using TRNSYS. It was confirmed that the proposed control method reduced the reheat coil energy consumption by about 20% and the air supply fan energy consumption by about 17% compared to the existing control method.

Ground heat accumulation caused by imbalanced heating and cooling loads in a building can cause the heat-source temperature to increase as the operating age of a geothermal heat pump (GHP) system increases. An alternative system to improve upon this situation is the hybrid GHP system. This study reviews existing research on GHP systems and hybrid GHP systems, variable water flow (VWF) control, and coefficient of performance (COP) prediction. Generally, constant flow control is applied to the circulating pump to provide a flow rate according to the maximum load. The need for VWF control was identified because the hybrid GHP system is used mainly as a heating and cooling heat source system for partial loads rather than the entire building load. Previous studies on predicting the COPs of GHP systems developed prediction models by selecting input values based on mathematical models, collecting data through multiple measurement points, and utilizing data from production environments. The model can be limited by the field environment, and it is necessary to predict the COP using machine learning based on existing field monitoring data. [ABSTRACT FROM AUTHOR]

In South Korea, the evaluation criteria for the shading devices installation are defined by regulations, but the standards of design methods are not clearly established. The installation of shading device is used to solar control in building. It has become mandatory for some public buildings due to revised regulations. Generally this device mean is horizontal shading at upper window. Therefore, a design of horizontal shading device which takes into account the energy consumption of the building is required, and indoor environmental problems which may occur due to the installation of them should also be taken into consideration. This research studied to propose a design which takes into account the energy consumption which may occur if the horizontal shading device is installed, and suggest an improved design method of horizontal shading devices on the basis of the analysis of the problems that may occur when they are installed. Consequently, it was confirmed that as the length of the horizontal shading device becomes longer, the incoming daylight is reduced and the indoor intensity of illumination becomes lower, and thus, more lighting energy may be consumed in a room where the shading device is installed than in the one where it is not. Therefore, annual energy consumption was calculated by applying the lighting control and it was found that the total energy consumption decreased as a result of the reduction in energy consumption for air conditioning and fans and further decrease in lighting energy consumption.

In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model. [ABSTRACT FROM AUTHOR]