一种公共建筑低碳改造优化方法及系统
By constructing a full life-cycle energy consumption and carbon emission benchmark model and a multi-objective particle swarm optimization algorithm, combined with effect matrix and robustness assessment, the problem of the synergistic effect of external environmental changes and technical measures in the low-carbon renovation of public buildings was solved, and scientific and reliable renovation decisions were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA UNIV OF TECH
- Filing Date
- 2026-01-23
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for optimizing low-carbon retrofitting of public buildings fail to effectively consider changes in the external environment and uncertainties, leading to biases in the assessment of energy conservation and emission reduction potential. Furthermore, they fail to accurately address the synergistic or antagonistic effects between different technological measures, resulting in a lack of scientific rigor in the optimization decision-making process.
We construct a full life-cycle energy consumption and carbon emission benchmark model that combines future climate and power grid data, use a multi-objective particle swarm optimization algorithm to screen retrofit measures, use the effect matrix to guide highly synergistic combinations, and combine robustness assessment and risk aversion coefficient to select the optimal retrofit scheme.
This improves the reliability of energy-saving potential assessment, ensures that the renovation plan has excellent performance and can withstand risks in long-term operation, and enhances the scientific nature and practical application value of decision-making.
Smart Images
Figure CN122048586B_ABST