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This study investigates the sustainable values of cafes established using idle industrial facilities that are a part of the cultural heritage of South Korea in terms of the characteristics of the architectural space and consumers’ space utilization. Twenty regenerative cafes in five regions were selected, and five of them were analyzed by comparing their characteristics with those of the conventional cafes. Unlike conventional cafes, regenerative cafes have architectural spaces that seem to be non-everyday and elicit a feeling of the passage of time. Users utilized these cafes as spaces for activities and experiences for long periods compared to conventional cafes. Consequently, regenerative cafes were found to contain sustainable values as complex networking spaces, as cultural heritage that can be experienced and as independent tourist destinations. Regenerative cafes have become unique differentiated architectural spaces utilized by several users.
Jun-Sik Eom; Sung-Hoon Yoon; Dai-Whan An. The Sustainability of Regenerative Cafes Utilizing Idle Industrial Facilities in South Korea. Sustainability 2021, 13, 4784 .
AMA StyleJun-Sik Eom, Sung-Hoon Yoon, Dai-Whan An. The Sustainability of Regenerative Cafes Utilizing Idle Industrial Facilities in South Korea. Sustainability. 2021; 13 (9):4784.
Chicago/Turabian StyleJun-Sik Eom; Sung-Hoon Yoon; Dai-Whan An. 2021. "The Sustainability of Regenerative Cafes Utilizing Idle Industrial Facilities in South Korea." Sustainability 13, no. 9: 4784.
For improving control methods in the thermal environment, various algorithms have been studied to satisfy the specific conditions required by the characteristics of building spaces and to reduce the energy consumed in operation. In this research, a network-based learning control equipped with an adaptive controller is proposed to investigate the control performance for supply air conditions with maintaining the levels of indoor thermal comfort. In order to examine its performance, the proposed model is compared to two different models in terms of the patterns of heating and cooling energy use and the characteristics of operational signals and overshoots. As a result, the energy efficiency of the proposed control has been slightly decreased due to the energy consumption increased by precise controls, but the thermal comfort has improved by about 10.7% more than a conventional thermostat and by about 19.8% more than a deterministic control, respectively. This result can contribute to the reduction of actual installation and maintenance costs by reducing the operating time of dampers and the energy use of heating coils without compromising indoor thermal comfort.
Sung Hoon Yoon; Jonghoon Ahn. Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season. Energies 2020, 13, 6023 .
AMA StyleSung Hoon Yoon, Jonghoon Ahn. Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season. Energies. 2020; 13 (22):6023.
Chicago/Turabian StyleSung Hoon Yoon; Jonghoon Ahn. 2020. "Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season." Energies 13, no. 22: 6023.
While the social sustainability of built environments is an essential aspect of architectural design education, systemic experiments still lack empirical pedagogy. Therefore, factors of social sustainability are hardly reflected in students’ projects seamlessly. To overcome such limitations, this study investigates the applicability and effectiveness of human behavior simulation. To ensure authentic architectural design, the projects were equipped with autonomous, rational anthropomorphic computer agents called virtual users (VUsers). This study compared the performance scores on social sustainability factors, assessed by the students who conducted design projects both before (without) and after (with) using the simulation. A one-way analysis of variance indicated that human behavior simulation promoted the performance of projects with respect to the parameters of accessibility and safety, ergonomic usability for heterogeneous users and supportability of social interactions. However, the simulation was not found to be effective in promoting the physical attractiveness of built environments and in ensuring the completeness of design solutions. Based on previous studies, the present study interpreted the reasons why the operability of VUsers and built environments, representations of emerging interactions of VUsers and whole-and-part analytics promoted explicit experimentation, but the factors of physical attractiveness and completeness were irrelevant to the rational examinations in the use of the simulation.
Seung Hong; Hwanjin Kim; Yongjun Song; Sung Yoon; Jaewook Lee. Effects of Human Behavior Simulation on Usability Factors of Social Sustainability in Architectural Design Education. Sustainability 2020, 12, 7111 .
AMA StyleSeung Hong, Hwanjin Kim, Yongjun Song, Sung Yoon, Jaewook Lee. Effects of Human Behavior Simulation on Usability Factors of Social Sustainability in Architectural Design Education. Sustainability. 2020; 12 (17):7111.
Chicago/Turabian StyleSeung Hong; Hwanjin Kim; Yongjun Song; Sung Yoon; Jaewook Lee. 2020. "Effects of Human Behavior Simulation on Usability Factors of Social Sustainability in Architectural Design Education." Sustainability 12, no. 17: 7111.
The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.
Dongkyu Lee; Jinhwa Jeong; Sung Hoon Yoon; Young Tae Chae. Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network. Energies 2019, 12, 3247 .
AMA StyleDongkyu Lee, Jinhwa Jeong, Sung Hoon Yoon, Young Tae Chae. Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network. Energies. 2019; 12 (17):3247.
Chicago/Turabian StyleDongkyu Lee; Jinhwa Jeong; Sung Hoon Yoon; Young Tae Chae. 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network." Energies 12, no. 17: 3247.
A ground source heat pump system (GSHPS) utilizes a relatively stable underground temperature to achieve energy-saving for heating and cooling in buildings. However, continuous long-term operation will reduce the soil temperature in winter, resulting in a decline in system performance. In this research, in order to improve the system performance of a GSHPS, a ground heat pump system integrated with solar thermal storage was developed. This solar-assisted ground heat pump system (SAGHPS) can both maintain the balance of the soil temperature effectively and achieve higher system performance than the conventional system. In this paper, in order to examine the characteristics of the system, a dynamic simulation was conducted under various conditions. The results of our case study provide specific operation data such as heat exchange rate, heat source temperature, and heat pump COP. As a result, the heat pump COP of SAGHPS was 4.7%, 9.3% higher than that of the GSHPS.
Yu Jin Nam; Xin Yang Gao; Sung Hoon Yoon; Kwang Ho Lee. Study on the Performance of a Ground Source Heat Pump System Assisted by Solar Thermal Storage. Energies 2015, 8, 13378 -13394.
AMA StyleYu Jin Nam, Xin Yang Gao, Sung Hoon Yoon, Kwang Ho Lee. Study on the Performance of a Ground Source Heat Pump System Assisted by Solar Thermal Storage. Energies. 2015; 8 (12):13378-13394.
Chicago/Turabian StyleYu Jin Nam; Xin Yang Gao; Sung Hoon Yoon; Kwang Ho Lee. 2015. "Study on the Performance of a Ground Source Heat Pump System Assisted by Solar Thermal Storage." Energies 8, no. 12: 13378-13394.