On the Application of BIM Technology and Neural Network Algorithm in the Study of Energy-Saving Optimization of the Envelope Structure of Assembled Buildings in the Western Sichuan Plain

• Published in: Journal of sensors Vol. 2022; pp. 1 - 12
• Main Authors: Li, HuaDong, Li, XuXiang, Jiang, Ying, Wei, Chuan
• Format: Journal Article
• Language: English
• Published: New York Hindawi 31.08.2022; John Wiley & Sons, Inc
• Subjects: Air conditioning; Algorithms; Architecture; Assembly; Building construction; Building envelopes; Building information modeling; Carbon; Computer simulation; Construction accidents & safety; Construction costs; Construction industry; Cost control; Design; Economic development; Energy conservation; Energy consumption; Energy efficiency; General contractors; Green buildings; Information technology; Marketing; Neural networks; Optimization; Planning; Project management; Qualitative research; China
• ISSN: 1687-725X; 1687-7268; 1687-7268
• DOI: 10.1155/2022/1653838

With the rapid development of the economy, carbon neutral and carbon peak are on the agenda in China, and the reform of intelligent construction and assembly building is in full swing in the Chinese construction industry. In order to further explore and study the relationship between the textural structural issues of the envelope structure and the overall energy consumption situation of the house, and further bridge the gap between the actual energy-saving design scheme and the actual application scheme, therefore, this paper takes a certain place as a research object to study the phenomenon of design assembly energy saving, based on dozens of assembly building enterprises researched, selected samples of envelope components studied, selected BIM technology for premodeling optimization, used neural network algorithm for analysis of influencing factors, and studied the application in the study of energy-saving optimization of assembly building envelope in the western Sichuan plain area, and used OpenStudio was used to build a model to simulate the heating and air conditioning load of the assembled building numerically and to fit the quantitative relationship between the variable factors and the building energy consumption. On this basis, the relationship between multifactor and single-factor variables is identified and evaluated using relevant variables means.