Reducing carbon emissions from the construction industry has been vital to addressing the growing global environmental change challenge. Building energy consumption data is crucial to many applications, such as carbon emission auditing, energy efficiency improvement, etc. This study aims to mine the energy consumption patterns of commercial buildings and explore the applicability of a data-driven model in building carbon emissions prediction. This research selected Dalian's large-scale green commercial building as a case study. The electrical load data for the past four years were collected, and the indoor and outdoor environmental data were monitored under different seasons. Machine learning was used to develop building carbon emissions forecasting models. The average annual increase rate of building electricity consumption before the pandemic was 5.9%. Miscellaneous electric loads (MELs) is the largest electricity consumer in the target building. On typical days, indoor illuminance and CO2 are highly correlated under different seasons. A forecasting model based on ensemble learning is found to have certain advantages in building carbon emissions prediction.

Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating perfor-mance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and ana-lyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building perfor-mance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO2/m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfac-tion was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions.(c) 2022 Elsevier B.V. All rights reserved.

With the urbanization level advancing in cities, increasingly significant urban ecological environment problems must be solved. The construction of a smart city with the overall development of information technology also regards environmental friendliness as the primary goal. The “smart” idea of urban environment innovation and governance has become a new model. In this paper, we first expound on the development process of low-carbon cities, eco-cities, and smart cities in Japan and China. Then, we analyze the coordinated development of intelligent environmental protection measures in government policies, transportation, energy utilization, resource recovery, and community management. Finally, we compare Japan and China’s smart city development characteristics. We discuss the improvement measures for energy utilization, urban transportation, and urban operation, including developing renewable energy systems, efficient energy use, and citizen participation policy. These experiences can provide feasible measures for constructing Asian smart cities and have great significance for the city’s sustainable applications and practice.

生态产业园作为公共建筑,存在人员流动性大、数据采集困难及环境质量与设计期望不符等问题.本文选取大连市某生态产业园绿色建筑作为研究对象,通过夏季监测室内空气质量参数和使用者满意度问卷调研,分析不同空间类型的办公区域室内空气的品质变化,并与绿色建筑评价标准对比分析.生态产业园绿色建筑室内环境运行情况的良好把控,对实现产业园整体的可持续性设计具有重要的支撑作用.