دورية أكاديمية
Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding
| العنوان: | Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding |
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| المؤلفون: | Changhwan Jang, Hong-Gi Kim, Byeong-Hun Woo |
| المصدر: | Materials, Vol 17, Iss 9, p 2108 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Technology LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Engineering (General). Civil engineering (General) LCC:Microscopy LCC:Descriptive and experimental mechanics |
| مصطلحات موضوعية: | Bayesian, threshold, smRNN, concrete, power consumption, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85 |
| الوصف: | Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R2 value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 17092108 1996-1944 |
| Relation: | https://www.mdpi.com/1996-1944/17/9/2108; https://doaj.org/toc/1996-1944 |
| DOI: | 10.3390/ma17092108 |
| URL الوصول: | https://doaj.org/article/168c5e0160964998856d4159eac49b49 |
| رقم الأكسشن: | edsdoj.168c5e0160964998856d4159eac49b49 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17092108 19961944 |
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| DOI: | 10.3390/ma17092108 |