Data-Driven Energy Retrofit Decision Support for Existing Campus Buildings

Campus building portfolios are heterogeneous and retrofitting them under limited budgets requires careful prioritization. This study develops a data-driven decision support framework that estimates retrofit potential from metered consumption data and ranks measures by a combined criterion of energy savings, carbon reduction and payback period.

研究背景

校园既有建筑群在建筑年代、围护结构、用能模式上差异显著，在有限预算下确定改造优先级具有挑战。

方法

构建数据驱动的改造决策支持框架：

• 基于计量数据反演建筑能耗基准与围护结构热工参数；
• 建立改造措施库，量化各项措施的节能潜力与碳减排量；
• 以节能率、碳减排量与投资回收期为综合指标进行排序优化。

结论

在某校区 32 栋建筑上的应用表明，所提方法给出的改造优先级排序在相同预算下较经验排序多实现约 15% 的节能收益。

@article{S.He20252025-ene,
  title   = {Data-Driven Energy Retrofit Decision Support for Existing Campus Buildings},
  author  = {S. He and Y. Chen},
  journal = {Energy and Buildings},
  year    = {2025},
  doi     = {10.1016/j.enbuild.2025.100005},
}