Strategies for robust renovation of residential buildings in Switzerland.

Building renovation is urgently required to reduce the environmental impact associated with the building stock. Typically, building renovation is performed by envelope insulation and/or changing the fossil-based heating system. The goal of this paper is to provide strategies for robust renovation considering uncertainties on the future evolution of climate, energy grid, and user behaviors, amongst others by applying life cycle assessment and life cycle cost analysis. The study includes identifying optimal renovation options for the envelope and heating systems for building representatives from all construction periods that are currently in need of renovation in Switzerland. The findings emphasize the paramount importance of heating system replacements across all construction periods. Notably, when incorporating bio-based insulation materials, a balance emerges between environmental impact reduction and low energy operation costs. This facilitates robust, equitable, and low-carbon transformations in Switzerland and similar Northern European contexts while avoiding a carbon spike due to the embodied carbon of the renovation.

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters.

Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is traditionally considered. This work further assumes the existence of multiple and competing objective functions that need to be dealt with simultaneously. The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric. By introducing the concept of common random numbers, the resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II). The computational cost of such an approach is however a serious hurdle to its application in real-world problems. We therefore propose a surrogate-assisted approach using Kriging as an inexpensive approximation of the associated computational model. The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate the quantiles. Finally, the methodology is adapted to account for mixed categorical-continuous parameters as the applications involve the selection of qualitative design parameters as well. The methodology is first applied to two analytical examples showing its efficiency. The third application relates to the selection of optimal renovation scenarios of a building considering both its life cycle cost and environmental impact. It shows that when it comes to renovation, the heating system replacement should be the priority.