Building retrofit optimization considering future climate and decision-making under various mindsets

Abstract

Building retrofit is effective in reducing building energy use and improving comfort levels for existing buildings. However, conducting multi-objective optimization for individual buildings can be challenging due to the laborious computational cost of using white box models and the difficulty in visualizing and understanding the decision-making process. Additionally, the impact of climate change has not been fully considered for the post-retrofit lifecycle. This research proposes a pragmatic automated scheme that integrates a feature selection method based on marginal abatement cost analysis and variance-based sensitivity analysis, multi-objective optimization supported by non-dominated sorting differential evolution (NSDE) algorithm, tailor-made decision-making support under different mindsets, and tree-based retrospection scheme of decision-making pathways. The simulation engine used in this study is a low-order white box modeling tool developed by the research team. The proposed scheme was applied to two educational buildings with different thermal characteristics, and the results showed that a certain number of sampling sizes were needed to achieve reliable feature selection results. The hierarchical clustering based decision-making support scheme has demonstrated robustness in visualizing and supporting decision-making for Pareto front. Two retrofit mindsets - aggressive and balanced - were assumed in the decision-making process, and the proposed method produced distinct final solutions accoridng to the two mindsets. This framework can support informed decision-making, helping stakeholders implement sustainable practices and transition to a low-carbon built environment.

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