{"title":"Daylighting performance prediction model for linear layouts of teaching building clusters utilizing deep learning","authors":"","doi":"10.1016/j.scs.2024.105821","DOIUrl":null,"url":null,"abstract":"<div><p>Deep learning (DL) has proven to be an effective tool for predicting the daylighting performance of buildings on individual rooms or standalone buildings by utilizing a few straightforward design parameters as input variables for analysis. In addition to existing studies, exploring methods to characterize larger objects with spatial relationships may contribute to understanding the impact of layout on the overall daylighting performance of buildings. In this study, a DL model based on the framework of “Autoencoder-Based Feature Extraction with Artificial Neural Network (AE-ANN)” has been developed to predict the daylighting performance of the layout of teaching building clusters. In order to efficiently extract the layout characteristics and improve the model's generalization capabilities, an autoencoder (AE) was pre-trained to encode the planar layout images of teaching building clusters into feature vectors, which were then employed for training an ANN model. In the testing dataset, the AE-ANN model demonstrated impressive accuracy, achieving R² values of 0.946 for sDA and 0.853 for ASE, alongside MSE values of 0.312 and 0.656. This research investigated the feasibility of the AE-based model for predicting daylighting performance of large-scale scenarios, highlighting its potential as a fundamental model for the development of more intricate daylighting prediction models.</p></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724006450","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Deep learning (DL) has proven to be an effective tool for predicting the daylighting performance of buildings on individual rooms or standalone buildings by utilizing a few straightforward design parameters as input variables for analysis. In addition to existing studies, exploring methods to characterize larger objects with spatial relationships may contribute to understanding the impact of layout on the overall daylighting performance of buildings. In this study, a DL model based on the framework of “Autoencoder-Based Feature Extraction with Artificial Neural Network (AE-ANN)” has been developed to predict the daylighting performance of the layout of teaching building clusters. In order to efficiently extract the layout characteristics and improve the model's generalization capabilities, an autoencoder (AE) was pre-trained to encode the planar layout images of teaching building clusters into feature vectors, which were then employed for training an ANN model. In the testing dataset, the AE-ANN model demonstrated impressive accuracy, achieving R² values of 0.946 for sDA and 0.853 for ASE, alongside MSE values of 0.312 and 0.656. This research investigated the feasibility of the AE-based model for predicting daylighting performance of large-scale scenarios, highlighting its potential as a fundamental model for the development of more intricate daylighting prediction models.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;