AI-Driven Multi-Objective Optimization for Cost-Effective Design of Passive-Oriented Nearly Zero-Energy Building in Chengdu

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Multi-objective optimization using NSGA-II for building design is demonstrated with a clear methodology applicable to other regions.

🏢実務担当者:Provides a quantifiable decision-making tool for selecting cost-effective envelope parameters in passive NZEB design.

🏛政策担当者:Supports development of cost-optimized building energy codes by identifying trade-offs between energy savings and incremental costs.

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings.

• openalex https://doi.org/10.3390/buildings16081604first seen 2026-05-05 08:06:36 · last seen 2026-05-05 19:14:32