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This study investigates and analyses the energy consumption of two museums in Hangzhou, China. Life cycle analysis (LCA) is used to calculate the energy consumption and greenhouse gas emissions in the life cycles of two museums, thereby determining both their potential and feasibility of energy-saving. Three strategies of using solid walls instead of curtain walls, adding shading to skylights and curtain Walls, and controlling air conditioning temperature have been proposed for Museum A which has higher greenhouse gas emissions. And it would help to reduce CO 2 emissions in the life cycle to 30.58%, 4.81%, and 4.22% respectively according to the simulations. The study also offers guidance to the construction green museum architecture and its operation with low energy consumption. [ABSTRACT FROM AUTHOR]

• PV/WT production and complementarity in an urban environment were examined. • Hybrid PV–wind systems improve self-sufficiency and self-consumption ratio. • Richer wind resources enhance matching performance of hybrid energy systems. • Matching performance decreases with increasing building height. Rooftop photovoltaic (PV)–wind hybrid systems serve as a promising energy supply source to mitigate environmental concerns and satisfy high energy demands. Most of energy matching studies focused on the matching capability of photovoltaic generation with building load, and the application of wind power to complement PV was rarely considered. In the context, this study explored the production aspects of PV–wind turbine (WT) hybrid systems and their potential to meet building energy use demands in densely built urban areas. Both solar and wind resources in 18 cities in eastern China were classified into three energy output levels, and Hangzhou was selected as a representative city for analysis of the complementarity of the two resources. Based on the modeling of hybrid PV–wind system generation, a PV/WT production feature curve was generated by k-means clustering. The matching between renewable energy production and building energy use and the influences of different wind resources and building heights were analyzed for rooftop hybrid systems in two typical residential buildings. The results reveal that the wind and solar resources in Hangzhou are partly complementary in terms of time sequence and power output. For the multi-story and high-rise residential building considered, a rooftop hybrid PV–WT system was concluded to be beneficial in improving energy generation, reliability, and load matching performance in a limited space. The technical performance for the high-rise case was inferior to that for the multi-story case considered. In the case of residential buildings, the matching between on-site supply and load is influenced by the local wind resources and building height. The richer the urban wind resource, the greater the load cover ratio and the self-sufficiency of the installed rooftop hybrid system. However, the load matching performance declines with increasing building height. The proposed general method for analyzing rooftop hybrid PV–WT systems can enable widespread and efficient solar and wind energy utilization in densely populated and built urban areas. [ABSTRACT FROM AUTHOR]

Numerous existing neighbourhoods, such as many of those built-in 1980–2000 in China, have poor physical environmental conditions. Supplemented by field investigations, this paper puts forward an outdoor environment evaluation tool designed for existing neighbourhoods based on the multi-criteria decision-making (MCDM) method. Evaluation indicators of the acoustic, luminous, and thermal environments and air quality were chosen and classified according to the characteristics of existing neighbourhoods. The weight of each indicator was determined through opinions from both residents and experts. To achieve a comprehensive score, the calculation was carried out based on a multi-level system for neighbourhoods with different conditions/requirements. The proposed methodology was then applied to measure the outdoor environment performance of four existing neighbourhoods in Hangzhou, China. Results showed that, for all neighbourhoods, the outdoor environments were shown to be inadequate regarding acoustic and luminous environments, with improvement potential for the thermal environment and air quality. Corresponding suggestions for renovation and upgrading were put forward. The proposed tool can inform renovation potential of existing neighbourhoods from both residents' and experts' opinions, diagnosis of outdoor physical environments in four aspects, and development of renovation strategies under different environmental conditions/requirements. • Outdoor physical environment assessment tool proposed for existing neighbourhoods. • The tool was based on the multi-criteria decision-making method. • Evaluation indicators were classified into three levels based on different conditions. • Both residents and experts' opinions contributed to the weight of indicators. • The tool analysed renovation measures in 4 typical Chinese neighbourhoods. [ABSTRACT FROM AUTHOR]

Urban surface composition is key information for global carbon emission estimation, mesoscale numerical simulations, and outdoor environment studies at both city and neighborhood scales. High spatial-resolution satellite imagery can provide accurate urban surface data. Due to the large volume of data, it is time-consuming to extract manually information from satellite imagery or use other conventional classification methods. Deep leaning-based classification methods have great potential in satellite imaginary recognition due to their high efficiency and accuracy. In this study, we established a novel workflow for deep learning-based satellite imagery classification. At the stage of training dataset preparation, an object-based image analysis (OBIA) classification with the assistance of open source ancillary data from OpenStreetMap (OSM), i.e., OBIA-OSM method, was proposed to accelerate the procedure and improve the accuracy. An area of 33.5 km2 training dataset with 1 m spatial resolution was built based on the Gaofen2 satellite imagery of Hangzhou, China (GF2-HZ dataset). A Res-UNet + inception model was developed for the deep learning process. The results using the newly built model were compared with those from previous FCN and UNet models. The overall accuracy of our proposed model on the GF2-HZ dataset reached 83.1% which outperforms previous models. The influences of raw data-related factors, such as spectral bands composition and spatial resolution, were carefully tested. Data normalization and 'transfer learning' techniques were also analyzed and applied to improve the generalization ability of deep learning models. • A work flow for the deep learning based urban surface classification was established. • The OBIA-OSM method was proposed to make training dataset efficiently. • A Res-UNet + inception model was developed for the deep learning process. • The influences of raw data related factors were tested and analyzed. • The generalization ability of proposed deep learning models was discuss. [ABSTRACT FROM AUTHOR]

• Investigation of coupled heat and moisture transfer within complex structures. • Study objects are wall-to-floor thermal bridges and the surrounding areas. • Moisture in building envelopes degrades the thermal insulation performance. • Neglect of moisture transfer magnifies the phenomenon of thermal bridges. • Exterior thermal insulation causes moisture to accumulate in thermal bridges. A significant portion of the cooling or heating load of buildings is contributed by relatively large heat flux through thermal bridges. Water vapour concentration significantly alters thermal conductivity and heat transfer characteristics within the thermal bridges, affecting building energy consumption, particularly in humid climates. Moreover, the condensation on the building envelope is highly related to the temperature and moisture distribution, which threatens the safety of the building structure. The heat and moisture transfer presents strong coupling effects. Nevertheless, the coupled heat and moisture transfer (HAMT) within thermal bridges and its influence on the building energy consumption were less studied. In this study, field experiments and validated numerical models were conducted to investigate the heat and moisture transfer in the wall-to-floor thermal bridge (WFTB) in a humid and hot summer cold winter region of China (Hangzhou city). The results show that the heat transfer is underestimated if the moisture is not considered. The differences in heat transfer between the WFTB and the main part of the wall were reduced when considering the coupling effect of heat and moisture transfer, as the thermal performance of the wall is more sensitive to moisture than that of the WFTB region. Furthermore, if the significant moisture transfer is considered, the overall thermal conductivity of the WFTB has a risk of counterintuitively increasing when a thermal insulation layer is added. The reason for this is that the thermal insulation layer may raise the moisture content in the WFTB region, subsequently increasing the thermal conductivity. [ABSTRACT FROM AUTHOR]