A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting

Yizhe Xu, Chengchu Yan, Hao Qian, Liang Sun, Gang Wang and Yanlong Jiang
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Yizhe Xu: Key Laboratory of Aircraft Environment Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China
Chengchu Yan: College of Urban Construction, Nanjing Tech University, No. 200 North Zhongshan Road, Nanjing 210009, China
Hao Qian: Qinhuai Education Financial Assets Management Center, Nanjing 210002, China
Liang Sun: Civil Aviation Management Institute of China, No. 3 Huajiadi East Road, Chaoyang District, Beijing 100102, China
Gang Wang: Nanjing Zhongyi Architectural Design Institute Co., Ltd., Nanjing 210009, China
Yanlong Jiang: Key Laboratory of Aircraft Environment Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China

Sustainability, 2021, vol. 13, issue 23, 1-19

Abstract: The classroom environment is of great significance for the health of primary and secondary school students, but a comfortable indoor environment often requires higher energy consumption. This paper presents a multi-objective optimization method based on an artificial neural network (ANN) model, which can help designers efficiently optimize the design of primary and secondary school classrooms in southern China. In this optimization method, first, the optimization objectives and variables are determined according to building characteristics, and the physical model is established through simulation software (EnergyPlus) to generate the sample space. Second, sensitivity analysis is carried out for each optimization variable, and the physical model is modified according to the results to regenerate the sample space. Third, the ANN model is trained by using the regenerated sample space, and the Pareto optimal solution is generated through the use of the non-dominated sorting genetic algorithm II (NSGA-II). Finally, the effectiveness of the multi-objective optimization method is proven through a typical case of primary and secondary school classrooms in Nanjing, China. The results show that, compared with the benchmark scheme, TES decreased by 810.8 kWh at most, PT increased by 47.8% at most and DI increased by 4.2% at most.

Keywords: envelop optimization; ANN; school classroom; multi-objective (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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