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The airtightness of a building has a significant impact on energy savings, structural longevity, and indoor air quality for occupants. Therefore, it is essential to accurately measure the airtightness of buildings, though the widely used fan pressurization method suffers from several shortcomings. For this reason, transient methods have recently emerged to assess airtightness by monitoring pressure changes over time, but studies using transient methods in this field are rare. In this study, we selected three representative buildings to conduct field tests to verify the practical applicability of the improved transient method. To verify the results of the transient method, we conducted a comparison experiment with the blower door test: a widely used measurement method. When measuring the effective leakage area, the average standard deviation of the transient method was 0.903 cm2, which was much smaller than the blower door test result of 1.488 cm2. In addition, the recorded standard errors ranged from 0.197 cm2 to 0.816 cm2 for the transient method and from 0.269 cm2 to 1.801 cm2 for the blower door test. Notably, the transitional method was more reproducible than the blower door test while showing similar accuracy. Given these results, it is expected that the improved transitional method can be used to evaluate airtightness performance in the field. [ABSTRACT FROM AUTHOR]

Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green & Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix. [ABSTRACT FROM AUTHOR]

The amount of heat lost through the envelope of a building is one of the most important variables that affects the energy performance evaluation of a house. In addition, it is especially important to estimate and accurately diagnose the amount of heat produced by windows. In Korea, windows' U-values reflect a building's initial design values and thermal characteristics that determine the thermal performance of an existing building, and is a factor that can overestimate the energy performance of a building. Therefore, there is a need for a field measurement method that can accurately measure the total U-value of windows in an existing house. This study provides a method of quantitatively measuring the total U-value of windows using the infrared (IR) method in ISO 9869-2. As a result of measuring the U-value using the infrared (IR) method, the Korean Standard (KS F 2278) for window performance test result values and the root mean square error (cvRMSE) for the U-value measurements using the IR method showed a high accuracy of about 3.29%. In addition, we confirmed that the IR method is an effective (cvRMSE about 7% improvement) method that can measure the comparison result faster than the heat flow meter (HFM) method, which is a conventional thermal performance measurement method. [ABSTRACT FROM AUTHOR]

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently. [ABSTRACT FROM AUTHOR]

The interest in zero energy buildings is increasing in South Korea. Zero energy buildings need to save energy by using passive technology. The window performance is important to the thermal insulation of the building. Also, the government regulates the window performance through regulation and standards. However, it is difficult to predict window performance because the components of the window have become complicated due to the various materials used in the glass and frame. Based on window performance standards and regulations, the quality of window performance was managed. In this research, to consider thermal performance in proper window design in South Korea, we confirmed the impact on the thermal performance of the window through various kinds of materials and shapes. The authors also propose a window shape classification and frame calculation method based on actual test results. The authors analyzed the thermal performance data of the windows provided by the Korea Energy Agency and confirmed the change in the thermal performance of the windows by year and by frame material. The average U-value of the window decreased from 2012 to 2015 and maintained similar values until 2017. In 2018, this value was decreased to comply. Also, the authors confirmed the U-value of the windows through actual physical experiments and confirmed the change in thermal performance by the construction of the windows based on the results. The results show, in the case of aluminum windows, the U-value corresponding to Grade 3 (1.4–2.1 W/m2·K) was as high as about 60%. Regarding the analyzed results of the U-values of PVC windows, Grade 3 (U-value of 1.4–2.1 W/m2·K) accounted for about 35%, and Grade 2 (U-value of 1.0–1.4 W/m2·K) for about 29%. This paper also confirmed that the frame U-value of the PVC windows is lower than the frame U-value of the aluminum windows. Therefore, the authors proposed the performance index of the glazing part in PVC and aluminum window design. The results of this research can be used as basic data to identify problems in the method of determining the performance of windows in Korea. [ABSTRACT FROM AUTHOR]