Non-dominated sorting genetic algorithm-II: A multi-objective optimization method for building renovations with half-life cycle and economic costs
Xiaoxiang Zhan, Weirong Zhang, Ruijun Chen, Yifei Bai, Jingjing Wang, Gaofeng Deng
Abstract
In this paper, we present a comprehensive optimization framework that identifies renovation plans to minimize half-life cycle carbon emissions, investment payback period , and indoor discomfort hours. The framework consists of four stages. First, relevant data were collected, building models were established, and the renovation scope and preliminary parameters were determined. Second, a sensitivity analysis of the initial parameter set was conducted, and important parameters were selected and input into a back-propagation neural network model for prediction. Finally, an optimal renovation plan was obtained through multi-objective optimization and the technique for order of preference by similarity to the ideal solution (TOPSIS) decision-making. To illustrate the framework's feasibility, it was applied to a building as an example. Remarkably, carbon emissions were reduced by 82.2 %, and zero carbon was achieved during the half-life cycle. Moreover, this achievement resulted in a relatively swift payback period of 3.9 years, coupled with a commendable 30 % decrease in indoor discomfort hours. Hence, the framework is effective in optimizing building renovation objectives, yielding a more harmonious and ideal building renovation strategy, and can be widely utilized to enhance building performance.