The improvement of building and living conditions in high-cold areas has always been an issue worthy of attention, but there is currently no research using field survey data for evaluation. The Ganzi region, based in the western plateau of China, is a typical example for such a study. Restricted by factors such as natural conditions and economic level, the winter indoor thermal environment of western plateau houses is generally poor. Taking the new residential houses in the Ganzi region as a case study, the authors of this paper conducted field research and analyses. First, the authors analyzed the construction technology and functional layout of the building through thermal environment testing and investigation; second, the authors analyzed the user’s activity path according to the production and lifestyle; thirdly, the authors comprehensively evaluated the indoor thermal comfort through questionnaires and a predicated mean vote (PMV)-predicted percentage dissatisfied (PPD) evaluation model. The research results showed that: (1) the construction technology, functional layout, and temperature distribution of the new residential building were consistent with the user’s activity path, which could effectively improve thermal insulation ability and thermal comfort; (2) compared to the developed eastern regions, the users in the building showed a stronger tolerance and wider acceptable temperature range in the extreme climate environment; and (3) under certain cooperative work conditions, an indoor temperature of 10–14 °C could meet basic thermal environment requirements and thus lower the limits of the standards. The author’s method was proven to be more resilient than current standards in dealing with climate change. Therefore, this research can provide a practical reference for the improvement of peoples’ living conditions and sustainable development in cold regions and other harsh areas.

The relationship between environmental factors and the indoor physical environment is very close, and external shading is considered an effective way to adjust the interaction between the indoor and outdoor environment. However, determining how to set up an external shading system remains a notable issue. In the early design stage, architects have adopted the process of designing the form and function first and then checking whether those characteristics meet the energy-saving specifications. However, this process involves a great deal of repetitive and inefficient work and cannot meet the requirements of energy savings and emission reductions in a global context. Therefore, it is particularly important to seek a design method that combines energy-saving design with form-based design. This paper takes a construction project in Northwest China as its research object. In this study, typical parametric models for external shading are designed. Furthermore, indoor performance objectives based on light environment analysis are proposed, and Ladybug Tools and the genetic algorithm (GA) are used for optimization and verification. The optimization results show that the adaptive shading system can significantly reduce the total cooling energy consumption per unit area in summer by 20% and 15%, respectively. The comfort level throughout the year improved by 14.8% (air conditioning on) and 4.7% (air conditioning off). This study proposes a fast and effective shading parametric design method for architects in the early stage, improving the efficiency and accuracy of performance-based design.

Under the impact of COVID-19 and the needs for urban expansion, a large number of outpatient buildings have been rapidly constructed, but the problem of high energy consumption has always been ignored. There is a lack of research on the adaptability of building shape in different climate zones. Many studies have shown that a reasonable shape in the early stage of design can significantly reduce the energy consumption of buildings. Therefore, it helps if architects quickly select a reasonable shape that can effectively reduce energy consumption. This study summarized a number of outpatient building cases in China and proposed three typical building shapes: centralized-type (Shape-1), corridor-type (Shape-2), and courtyard-type (Shape-3). The Design Builder tool was used to simulate and analyze the typical building energy consumption in different climate zones. The simulation results show that Shape-2 (angle: 0°) should be chosen in severe cold zone; Shape-1 (angle: 90°) should be chosen in cold zone; Shape-1 (angle: 0°) should be chosen in hot summer and cold winter zone; Shape-1 (angle: 60°) should be chosen in hot summer and warm winter zone; and Shape-1 or Shape-2 can be chosen in warm zone. The results of this study can provide suggestions for the energy saving design of outpatient buildings in China and other areas with similar conditions. The result can help architects make rapid shape selection in the early stage of design.

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.