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Volume 53 Issue 7
Jul.  2023
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Article Contents
FANG Tao, LIU Ruijie, WANG Yanzheng, ZOU Ran. Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 16-24,78. doi: 10.13204/j.gyjzG22120509
Citation: FANG Tao, LIU Ruijie, WANG Yanzheng, ZOU Ran. Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 16-24,78. doi: 10.13204/j.gyjzG22120509

Comprehensive Optimization and Empirical Verification of Office Building Design Parameters in Cold Regions Based on Nearly Zero Energy Consumption Targets

doi: 10.13204/j.gyjzG22120509
  • Received Date: 2022-12-05
  • At present, the nearly zero energy consumption building is showing the trend of scale promotion in China, and the design and evaluation standards from the national to the local have been gradually improving, and most of the standards limit the value of energy-saving design parameters to achieve the goal of near-zero energy consumption. However, office buildings have diverse demands for energy consumption and complex structure of energy consumption, so it is necessary to comprehensively optimize the energy-saving design parameters to achieve the design goal of energy consumption minimization. Based on this, the paper firstly constructed the base model of energy consumption composition and building information for the office buildings in cold regions through investigation, and constructed a parameterized integrated platform with model construction, energy consumption simulation and automatic optimization through grasshopper; secondly, a comparative study of office buildings with different thermal design parameters of building envelopes was conducted, and the importance of lighting energy consumption and the influence law of total energy consumption by multi-parameter interaction with this platform was determined; then, on this basis, the paper optimized the energy consumption-oriented design parameters by genetic algorithm, and the optimal results were obtained; thirdly, with the Comprehensive Lab Building of Shandong Jianzhu University as an example, the paper verified the reliability and accuracy of the research results by comparing the measured data with the simulation data; finally, a correlation analysis of design parameters and energy consumption results was conducted by data statistics software, and a machine learning model was established by training data sets in MATLAB, thus verifying the accuracy of the model.
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