The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Physical energy and data-driven models in building energy prediction: A review
Open accessAbstract
The difficulty in balancing energy supply and demand is increasing due to the growth of diversified and flexible building energy resources, particularly the rapid development of intermittent renewable energy being added into the power grid. The accuracy of building energy consumption prediction is of top priority for the electricity market management to ensure grid safety and reduce financial risks. The accuracy and speed of load prediction are fundamental prerequisites for different objectives such as long-term planning and short-term optimization of energy systems in buildings and the power grid. The past few decades have seen the impressive development of time series load forecasting models focusing on different domains and objectives. This paper presents an in-depth review and discussion of building energy prediction models. Three widely used prediction approaches, namely, building physical energy models (i.e., white box), data-driven models (i.e., black box), and hybrid models (i.e., grey box), were classified and introduced. The principles, advantages, limitations, and practical applications of each model were investigated. Based on this review, the research priorities and future directions in the domain of building energy prediction are highlighted. The conclusions drawn in this review could guide the future development of building energy prediction, and therefore facilitate the energy management and efficiency of buildings.
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Cited by
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105287 · Cited by 299
Zheng, P., Zhou, H., Liu, J., & Nakanishi, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121607 · Cited by 118
Kim, D., Lee, J., Do, S., Mago, P. J., Lee, K. H., & Cho, H. (2022) · Energies · doi:10.3390/en15197231 · Cited by 111
Zhang, L., Fu, X., Li, Y., & Chen, J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106244 · Cited by 40
Hu, Y., Qin, L., Li, S., Li, X., Zhou, R., Li, Y., & Sheng, W. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113429 · Cited by 22
Metadata
- Authors
- Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y.
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 8
- Pages
- 2656-2671
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W4211076903
- Cited by
- 384
- References
- 92
- FWCI
- 34.38
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chen, Y., Guo, M., Chen, Z., Chen, Z., & Ji, Y. (2022). Physical energy and data-driven models in building energy prediction: A review. Energy Reports. https://doi.org/10.1016/j.egyr.2022.01.162
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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