.png){kind=icon}
.png){kind=icon}
Return
Multi-objective optimization designs of phase change material-enhanced building using the integration of the Stacking model and NSGA-III algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.est.2023.107807.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The incorporation of phase change materials (PCM) into building envelopes has proven to reduce carbon emissions and energy consumption to combat climate change. However, the energy performance of PCM-enhanced building depends on several factors and the optimization of such factors using empirical building design is more difficult and sometimes even impossible. Therefore, this paper proposes a multi-objective optimization method to consider multi-objectives, including building energy consumption, economic benefit, and carbon-saving. In this study, a non-dominated Sorting Genetic Algorithm III (NSGA III) is coupled with a Stacking model to minimize building operational energy consumption (BOEC) and maximize life cycle economic benefit (LCEB) and life cycle carbon reduction (LCCR) simultaneously by finding the optimum configuration of PCM thickness, window-to-wall ratio, exterior glazing U-value and solar heat gain coefficient. The results show that the Stacking model combined with 8 heterogeneous machine learning models has the best performance for predicting energy consumption with a high correlation efficiency (R-2 = 0.97). In addition, the building optimized with the Stacking-NSGA III framework shows a reduction of BOEC by 45.38 % and an increase of LCCR by 10.75 kg.CO2.e/m(2). Moreover, the LCEB over a 50-year service life is 452.21 CNY/m(2). It is believed that the proposed multi-objective optimization method can help stakeholders to find the most suitable PCM-building design strategies for their specific needs.
Keywords:
Phase change materials
Machine learning
Multi-objective optimization
Building operational energy consumption
Life cycle economic benefit
Life cycle carbon reduction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
2.2W
Citations:
10.1W
View more organizations