A target-driven decision-making multi-layered approach for optimal building retrofits via agglomerative hierarchical clustering: A case study in China

Abstract The optimization of energy, environmental and economic (3E) outcomes is the principal approach to identifying retrofit solutions for a sustainable built environment. By applying this approach and defining a set energy performance target, this study proposes a makeshift decision framework that integrates a data mining procedure (agglomerative hierarchical clustering (AHC)) into the decision-making process to provide a simplified 3E assessment of building retrofits on a macro-scale. The framework comprises of three model layers: (1) a building stock aggregation model, (2) an individualistic 3E model that provides the sensitivity analysis for (3) a life cycle cost-environmental assessment model. The framework is demonstrated and validated with a case study aimed at achieving the set energy targets for low-rise office buildings (LOB) in Shanghai. The model defines 4 prototypical buildings for the existing LOB blocks, which are used for the individual evaluation of 12 commonly applied retrofit measures. Subsequently, a simplified LCC-environmental assessment was performed to evaluate the 3E prospects of 2048 possible retrofit combinations. The results uniquely identify retrofit solutions to attain the set energy performance targets and optimal building performance. Furthermore, the decision criteria for different investment scenarios are discussed. Overall, this study provides building investors an innovative framework for a facile and holistic assessment of a broader range of retrofit alternatives based on set performance targets.