The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A short-term building cooling load prediction method using deep learning algorithms
Open accessAbstract
Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
A review on buildings energy consumption information
Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2007.03.007 · Cited by 6,649
Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“Fan et al. [168] used deep learning to enhance the performance of building cooling load prediction.”
BackgroundLago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642
A research agenda for augmented and virtual reality in architecture, engineering and construction
Davila Delgado, J. M., Oyedele, L., Demian, P., & Beach, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101122 · Cited by 486
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021) · Frontiers in Energy Research · doi:10.3389/fenrg.2021.652801 · Cited by 445
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Metadata
- Authors
- Fan, C., Xiao, F., & Zhao, Y.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 195
- Pages
- 222-233
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2595984151
- Cited by
- 692
- References
- 3
- FWCI
- 59.01
- Type
- journal article
- Language
- en
- Open access
- bronze
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
