Building energy consumption optimization method based on convolutional neural network and BIM
The increasing tension of energy supply and demand makes the optimization of building energy consumption more and more concerned by researchers. Based on the theory of convolutional neural network and BIM (Building Information Modeling), a building energy consumption optimization model is constructe...
| الحاوية / القاعدة: | Alexandria Engineering Journal |
|---|---|
| المؤلفون الرئيسيون: | , |
| التنسيق: | مقال |
| اللغة: | الإنجليزية |
| منشور في: |
Elsevier
2023-08-01
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://www.sciencedirect.com/science/article/pii/S1110016823005616 |
| _version_ | 1849314978162540544 |
|---|---|
| author | Fang Xu Qiaoran Liu |
| author_facet | Fang Xu Qiaoran Liu |
| author_sort | Fang Xu |
| collection | DOAJ |
| container_title | Alexandria Engineering Journal |
| description | The increasing tension of energy supply and demand makes the optimization of building energy consumption more and more concerned by researchers. Based on the theory of convolutional neural network and BIM (Building Information Modeling), a building energy consumption optimization model is constructed. The optimization parameter solving problem of convolutional neural network is solved. In the simulation process, a calculation model of the same size as Revit's three-dimensional model is established in eQUEST software, and the basic analysis parameters of the model, such as geographical location, meteorological data and other information, component materials, and running time table are set as unified standards. The energy consumption simulation analysis was carried out for the self-built model and the model automatically generated by the improved DOE-2 file in eQUEST software. The body coefficient of the building is 0.370, and the window-wall ratios in the east, west, south and north directions are 0.07, 0.21, 0.30 and 0.16, respectively, which all meet the requirements of relevant specifications. Compared with the scheme before optimization, it is found that building energy consumption is reduced by 24.53%, natural lighting is increased by 18.98%, and natural pressure par hours are increased by 10.57%. |
| format | Article |
| id | doaj-art-e0bf8d3fdbef4b33a2d7f8b3a2a1908c |
| institution | Directory of Open Access Journals |
| issn | 1110-0168 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| spelling | doaj-art-e0bf8d3fdbef4b33a2d7f8b3a2a1908c2025-09-03T02:03:05ZengElsevierAlexandria Engineering Journal1110-01682023-08-017740741710.1016/j.aej.2023.06.084Building energy consumption optimization method based on convolutional neural network and BIMFang Xu0Qiaoran Liu1Corresponding author.; College of Art, Shandong Jianzhu University, Jinan 250101, Shandong, ChinaCollege of Art, Shandong Jianzhu University, Jinan 250101, Shandong, ChinaThe increasing tension of energy supply and demand makes the optimization of building energy consumption more and more concerned by researchers. Based on the theory of convolutional neural network and BIM (Building Information Modeling), a building energy consumption optimization model is constructed. The optimization parameter solving problem of convolutional neural network is solved. In the simulation process, a calculation model of the same size as Revit's three-dimensional model is established in eQUEST software, and the basic analysis parameters of the model, such as geographical location, meteorological data and other information, component materials, and running time table are set as unified standards. The energy consumption simulation analysis was carried out for the self-built model and the model automatically generated by the improved DOE-2 file in eQUEST software. The body coefficient of the building is 0.370, and the window-wall ratios in the east, west, south and north directions are 0.07, 0.21, 0.30 and 0.16, respectively, which all meet the requirements of relevant specifications. Compared with the scheme before optimization, it is found that building energy consumption is reduced by 24.53%, natural lighting is increased by 18.98%, and natural pressure par hours are increased by 10.57%.http://www.sciencedirect.com/science/article/pii/S1110016823005616Convolutional neural network (CNN)BIMBuilding energy consumptionConsumption optimizationLow carbonEmission reduction |
| spellingShingle | Fang Xu Qiaoran Liu Building energy consumption optimization method based on convolutional neural network and BIM Convolutional neural network (CNN) BIM Building energy consumption Consumption optimization Low carbon Emission reduction |
| title | Building energy consumption optimization method based on convolutional neural network and BIM |
| title_full | Building energy consumption optimization method based on convolutional neural network and BIM |
| title_fullStr | Building energy consumption optimization method based on convolutional neural network and BIM |
| title_full_unstemmed | Building energy consumption optimization method based on convolutional neural network and BIM |
| title_short | Building energy consumption optimization method based on convolutional neural network and BIM |
| title_sort | building energy consumption optimization method based on convolutional neural network and bim |
| topic | Convolutional neural network (CNN) BIM Building energy consumption Consumption optimization Low carbon Emission reduction |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823005616 |
| work_keys_str_mv | AT fangxu buildingenergyconsumptionoptimizationmethodbasedonconvolutionalneuralnetworkandbim AT qiaoranliu buildingenergyconsumptionoptimizationmethodbasedonconvolutionalneuralnetworkandbim |