Comprehensive improvement of energy efficiency and indoor environmental quality for university library atriumd—A multi-objective fast optimization framework

Abstract

Low-carbon, energy-saving, and health have become a common trend for the whole of mankind. However, how to balance the relationship between energy-saving and healthy indoor environment is a key issue for sustainable building development. This paper extracted the prototypical form of university library atrium based on 44 library cases in Wuhan. A methodology verified with measured data for evaluating building performance was constructed, and the synergistic influence of spatial morphology parameters on the building energy efficiency (BEE) and indoor environmental quality (IEQ) was analyzed. Finally, a multi-objective fast optimization framework coupled with machine learning algorithms was used to achieve the optimal design of university library atrium. The results showed that the parameters that influence the building energy consumption, indoor thermal comfort, daylighting performance most were the height-to-width ratio, the skylight solar heat gain coefficient, and the sidewall window-to-wall ratio, respectively. The machine learning models predicted performance 400 times faster than traditional performance simulations. And compared with the worst-performance scheme, the maximum optimization rate of building energy consumption, indoor thermal comfort, daylighting performance was 29.46%, 10.46%, and 65.56%, respectively. The multi-objective fast optimization framework could provide guidance for policy makers and architects to performance-based design in the early design stages of university library atrium.

Keywords

University library atrium / Spatial morphology / Building energy efficiency / Indoor environmental quality / Machine learning / Multi-objective optimization

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