As cities are extremely vulnerable to the impacts of climate change, they are fundamental in addressing these changes. However, streets, which are external spaces accessed by citizens in daily life, play an important role in improving the urban environment and public health. This study considered Busan in South Korea as a case study to investigate street canyons, including street canyon geometries and tree configurations, of old, present, and new city centers. The influence of morphological factors on the microclimate and outdoor thermal comfort was evaluated using the ENVI-met program for extremely hot weather. Changes in the street width, street orientation, and street canyon aspect ratio had a significantly higher impact on the microclimate and thermal comfort index (p < 0.01). These results indicated that the orientation of the main street should be consistent with the prevailing wind direction of Busan. Further, the shading of adjacent buildings improved the outdoor thermal comfort and reduced the significance of tree configuration in deeper street canyons. In addition, tree height had a more significant impact on street environment than other tree configuration factors, especially when the tree height increased from 9 m to 12 m. We recommended that the thermal comfort level can be improved by dynamically adjusting the relationship between the planting distance and tree height in streets having shallow street canyons.

Since the advent of reforms and opening-up of China, the focus has been on urban development. However, rural development has garnered attention in recent years. This research explores energy performance improvement methods for rural houses in Xi’an, China. It aims to discuss the feasibility of designing a nearly zero-energy building (nZEB), based on typical residential rural housing in Xi’an, through proposing new construction methods and examining the strategies for the refurbishment of an existing house. Initially, a typical rural house was modelled based on data collected from a field survey and historical documents. Subsequently, suitable passive design strategies were explored in the rural house design both in terms of proposing new construction methods and examining the refurbishment strategies of an existing house. After implementation of the passive design, the annual energy demand was reduced from 112 kWh/m² to 68 kWh/m² (new construction) and from 112 kWh/m² to 85 kWh/m² (refurbished). Even though the passive design significantly reduced the energy demand of the house, it could not achieve the Chinese nZEB standard. Therefore, a photovoltaic (PV) system and a storage battery were incorporated to meet the standard. Eighty per cent of the south roof area of the newly constructed and refurbished house was installed with a PV system and a storage battery with a capacity of 50 kWh and 52 kWh, respectively. After installation of the proposed renewable energy, the annual energy demand from the house was decreased to 35 kWh/m² (new construction) and 51 kWh/m² (refurbished), which both achieved the Chinese nZEB standard (equal to or below 55 kWh/m²). The study shows the effectiveness of the methods used to design the nZEB and can be used to instruct the residents to build the nZEB in rural villages like Xi’an in China.

With the rapid advancement of computer graphics and three-dimensional modeling technology, the processing and optimization of three-dimensional (3D) models have become contentious research topics. In the context of mobile devices or web applications, situations may arise where it becomes necessary to load a 3D model with a substantial memory footprint in real-time or dynamically adjust the level of detail of a model based on the scene’s proximity. In such cases, it is imperative to optimize the original model to ensure smoothness and responsiveness. Due to the simplicity of their algorithm, quadric error metrics (QEMs) can deliver excellent results in simplifying 3D models while maintaining high efficiency. Therefore, QEM is widely employed in engineering applications within the realm of computer graphics development. Moreover, in the pursuit of enhanced quality and efficiency, numerous scholars have improved it based on QEM algorithms. This study aims to provide a systematic review and summary of the principles and applications of current research on QEM algorithms. First, we conducted a bibliometric analysis of 128 studies in related fields spanning from 1998 to 2022 using CiteSpace. This allowed us to sort QEM algorithms and gain insights into their development status and emerging trends. Second, we delve into the fundamental principles and optimizations of the QEM algorithms to provide a deeper understanding of their implementation process. Following that, we explore the advantages and limitations of the QEM algorithms in practical applications and analyze their potential in various domains, including virtual reality and game development. Finally, this study outlines future research directions, which encompass the development of more efficient error metric calculation methods, the exploration of adaptive simplification strategies, and the investigation of potential synergies with deep learning technologies. Current research primarily centers on enhancing QEM algorithms by incorporating additional geometric constraints to better differentiate between flat and irregular areas. This enables a more accurate determination of the areas that should be prioritized for folding. Nevertheless, it is important to note that these improvements may come at the cost of reduced computational efficiency. Therefore, future research directions could involve exploring parallel computing techniques and utilizing GPUs to enhance computational efficiency.

With increasing consumption of primary energy and deterioration of the global environment, clean energy sources with large reserves, such as natural gas, have gradually gained a higher proportion of the global energy consumption structure. Monitoring and predicting consumption data play a crucial role in reducing energy waste and improving energy supply efficiency. However, owing to factors such as high monitoring device costs, safety risks associated with device installation, and low efficiency of manual meter reading, monitoring natural gas consumption data at the household level is challenging. Moreover, there is a lack of methods for predicting natural gas consumption at the household level in residential areas, which hinders the provision of accurate services to households and gas companies. Therefore, this study proposes a gas consumption monitoring method based on the K-nearest neighbours (KNN) algorithm. Using households in a residential area in Xi’an as research subjects, the feasibility of this monitoring method was validated, achieving a model recognition accuracy of 100%, indicating the applicability of the KNN algorithm for monitoring natural gas consumption data. In addition, this study proposes a framework for a natural gas consumption prediction system based on a backpropagation (BP) neural network.

The problem of air pollution in Korea has become progressively more serious in recent years. Since electricity is advertised as clean energy, some newly developed buildings in Korea are using only electricity for all energy needs. In this research, the annual amount of air pollution attributable to energy under the traditional method in a dormitory building, which is supplying both natural gas and electricity to the building, was compared with the annual amount of air pollution attributable to supplying only electricity. The results showed that the building using only electricity emits much more air pollution than the building using electricity and natural gas together. Under the traditional method of energy supply, a residential solid oxide fuel cell cogeneration system (SOFC–CGS) for minimizing environmental pollution of the building was simulated. Furthermore, as a high load factor could lead to high efficiency of the SOFC–CGS, sharing of the SOFC–CGS by multi-households could increase its efficiency. Finally, the environmental pollution from using one system in one household was compared with that from sharing one system by multi-households. The results showed that the environmental pollution from sharing the system was relatively higher but still similar to that when using one system in one household.

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.