A macro-scale optimisation of zero-energy design schemes for residential buildings based on building archetypes

Abstract

Energy conservation and carbon emission reduction in the building sector have become a global concern in combating environmental issues. To achieve this target, developing low-energy buildings becomes an integral approach. Different from the case-by-case design for public buildings, the geometrical construction form of residential buildings are roughly the same and can be concluded in several representative categories, which leads to several similar paths to achieve energy-saving targets for buildings belonging to the same category. Aiming to determine the applicable design schemes for each archetypal building, the architectural typology method and simulation-based method were used in this research. Furthermore, an optimisation step was done the design schemes from both energy and economic perspectives. As demonstrated in Ningbo city as a case, this research usesthe number of floors, building shape coefficients, average floor areas, and window-to-wall ratiosas classification factors based on previous research and proposed nine representative building archetypes. Subsequently, 512 (29) groups of design schemes combined from nine energy-saving measures were simulated for each building archetype. Considering the energy performance indexes as anenergy-perspective constraintand the life-cycle cost of design scheme as aneconomic-perspective constraint, the design scheme was optimised. The limitation of this research is the neglect of immediate environment such as proximity to other buildings or natural elements such as trees that provided a subsequent research direction. Specifically, this research obtained optimal design schemes of energy-saving targets for individual residential building archetypes in China’s hot summer and cold winter zone. Broadly, this research proposed a comprehensive method for individual building assessment and optimisation that could be further used for a urban-scale energy planning and prediction.