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.

In central area of Liaoning province, the thermal performance of rural building envelopes are mostly non-standardized in Northern China, resulting in a significant heat loss. In this study, The objective was to propose some low-cost and energy-efficient solutions. Through a field of inves-tigations, heating energy consumption was saved by 20% and increased construction costs was less than 8000 RMB, which was acceptable for rural residents. In order to achieve this target, NSGA-II algorithm was integrated with Rhino+Grasshopper and EnergyPlus simulation kernel to establish a thermal performance optimization model for heat transfer of the rural building envelopes. Among the above Pareto optimal solutions, the recommended thickness of insulation layer for the room floors, ceilings, and external walls were 70mm, 50mm, and 40mm respectively. Furthermore, try to reduce the window to wall ratio as much as possible. Finally, based on the lower total cost, three technical solutions that rural residents could select according to their specific needs have been put forward.

In Northeast China’s severe cold regions, increasingly airtight rural dwellings face a critical challenge: traditional biomass-fueled heating and cooking generate severe indoor particulate matter (PM) pollution, creating a sharp trade-off between maintaining thermal comfort and ensuring safe indoor air quality through ventilation. While multi-objective optimization is widely applied to urban buildings, its use to develop practical, behavior-based ventilation strategies for resource-constrained rural dwellings in this context represents a significant research gap. This study integrates field measurements of occupant behavior and environmental parameters from 192 households with a coupled thermal-PM_2.5 predictive model. The NSGA-II genetic algorithm was employed to perform a multi-objective optimization, balancing PM reduction against thermal comfort. The optimization reveals that short, high-intensity ventilation bursts are allowed. A typical optimized event can reduce post-cooking PM_2.5 concentrations to near-guideline levels while maintaining the indoor temperature within the residents’ adaptive comfort zone. This research provides the first evidence-based, region-specific natural ventilation guidelines for these dwellings. The findings offer a practical, no-cost strategy to mitigate health risks from indoor air pollution without significant energy penalties, providing a theoretical basis for future smart ventilation system design.