Energy Saving Optimization of Commercial Complex Atrium Roof with Resilient Ventilation Using Machine Learning

• Published in: Smart cities (Basel) Vol. 6; no. 5; pp. 2367 - 2396
• Main Authors: Xu, Ao, Zhang, Ruinan, Yu, Jiahui, Dong, Yu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Air conditioning; Architecture; commercial complex atrium; Computational fluid dynamics; Design; Energy conservation; Energy consumption; Energy efficiency; energy-saving renovation design; Environmental impact; Genetic algorithms; geometric parameter; Green buildings; Machine learning; Mathematical models; Neural networks; Optimization; Quantitative analysis; Radiation; resilient ventilation; Shopping malls; Simulation; Thermal comfort; Ventilation
• ISSN: 2624-6511; 2624-6511
• DOI: 10.3390/smartcities6050108

Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption.