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The thermal performance of rural building envelopes is mostly non-standardized in Northern China, resulting in significant heat loss. In this study, we take, as an example, the central area of Liaoning province, with the objective of proposing some low-cost and energy-efficient solutions. Through our investigations, we found that heating energy consumption was reduced by 20% and construction costs increased by less than CNY 8000 (USD 1108), which can be accepted by rural residents. In order to achieve this target, the NSGA-II algorithm integrated with Rhino + Grasshopper and EnergyPlus simulation kernel was used to establish a thermal performance optimization model for the heat transfer of rural building envelopes in this severe cold region. Among the above-calculated Pareto optimal solutions, the recommended thickness of insulation layers for room floors, roofs, and external walls was about 70 mm, 50 mm, and 40 mm, respectively. Furthermore, we tried to reduce the window-to-wall ratio as much as possible. Finally, based on both the lower building renovation cost and energy-saving rate, three technical solutions from which rural residents could select, according to their specific needs, are put forward.

A large number of people in China still live in rural villages. The indoor environment of these rural dwellings directly affects the quality of life of the occupants. Nevertheless, constrained by the quality of dwelling construction, rural buildings have poorer indoor environments and, at the same time, have a higher operating energy consumption. However, inadequate attention has been given to the summer thermal environment in cold regions. This work has been carried out around the thermal environment of rural residences in cold regions during summer. Field measurements, questionnaires, and data analysis were used in this study. We recorded the indoor and outdoor thermal environment parameters on a typical summer day in the Linyi rural area. Moreover, the subjective sensations and thermal adaptive behaviors of the participants were recorded in detail with a questionnaire. Linear regression showed that the neutral temperature for residents in summer was 27.52 °C, with acceptable temperatures ranging from 25.14 °C to 29.9 °C. Age and gender differences were found to affect the occupants’ sensation of thermal comfort and humidity, as well as their thermal adaptive behavior. In addition, a thermal adaptive model has been constructed in the study, which will further enrich the thermal adaptive investigation and provide a scientifically sound reference for the renovation and development of the local rural areas.

Unlike urban dwellings, it is very common for elderly people to stay at home alone in Chinese rural families, and some families have three generations in the same house who are in different situations, and their different family patterns lead to different highly sensitive parameters of building energy consumption. This paper first selects the three most common family patterns based on a questionnaire survey. The measured energy consumption behavior and electrical parameters, energy consumption time, and basic building parameters were input into DesignBuilder to build three building simulation models, and these were verified by comparing the predicted and measured values of the residential month-by-month electricity consumption. The global sensitivity analysis was then conducted using DesignBuilder software to determine the interactions between the variables by using the second-order Sobol index for cooling load, heating load, and comfort of the models to obtain standardized regression coefficients (SRC) for each factor to determine the most sensitive parameters. The results show that the different household patterns had little influence on the ranking of highly sensitive factors for heating and cooling, but annual electricity consumption and discomfort in different household patterns had a significant influence on the ranking of highly sensitive factors. For example, model 1 showed the most sensitivity to general lighting power density when optimizing the total amount of electricity was the goal, while the one that had the greatest degree of influence on the total amount of electricity in model 2 and model 3 was equipment power density.

The highest concentration of PM2.5 in cold rural dwellings of Northeast China is often generated by using mini stoves for cooking and heating, which can directly influence human health. As of yet, little is known about the impact of different daily life behavior on PM2.5 diffusion and residents’ exposure in rural dwellings. In this study, the characteristics of indoor PM2.5 variation and diffusion in rural dwellings was described by measuring some rural dwellings and establishing a multi-zone network model. The calculated results indicated that the relative errors between theoretical calculated results and experimental measured results are within 10%. PM2.5 diffusion in a rural dwelling can be predicted. Furthermore, the impacts of daily life behavior on PM2.5 diffusion and exposure assessment can be analyzed. Through discussion, heating behavior is the most important factor causing high concentrations of PM2.5 in each room, followed by cooking, smoking, and cleaning. Door opening time can lead to different interzonal airflows and PM2.5 diffusion rates. By reducing the inner door opening time to less than 1 min, PM2.5 could decrease to 300 μg/m3. Door closing behavior could decrease risk that PM2.5 diffuses to bedrooms by more than 50%, and exposure of residents in bedrooms could reduce to 100 (μg·h)/m3 effectively.