The DOI matches its Crossref record; 162 of the 162 references it cites were found in a registry or an index.
A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis
Open accessAbstract
With the advent of the era of big data, buildings have become not only energy-intensive but also data-intensive. Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems. This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain. In general, data mining technologies can be classified into two categories, i.e., supervised data mining technologies and unsupervised data mining technologies. In this field, supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis. And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis. Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods. Based on this review, suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
A review on the prediction of building energy consumption
Zhao, H.-X., & Magoulès, F. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.02.049 · Cited by 1,937
A review of data-driven building energy consumption prediction studies
Amasyali, K., & El-Gohary, N. M. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.04.095 · Cited by 1,874
Ahmad, M. W., Mourshed, M., & Rezgui, Y. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.04.038 · Cited by 1,013
A review on time series forecasting techniques for building energy consumption
Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.085 · Cited by 1,006
A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.069 · Cited by 954
Katipamula, S., & Brambley, M. R. (2005) · HVAC&R Research · doi:10.1080/10789669.2005.10391123 · Cited by 936
A review of data-driven approaches for prediction and classification of building energy consumption
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., & Zhao, X. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.09.108 · Cited by 803
Applying support vector machines to predict building energy consumption in tropical region
Dong, B., Cao, C., & Lee, S. E. (2005) · Energy and Buildings · doi:10.1016/j.enbuild.2004.09.009 · Cited by 800
Cited by
An overview of machine learning applications for smart buildings
Alanne, K., & Sierla, S. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103445 · Cited by 280
Zhang, C., Li, J., Zhao, Y., Li, T., Chen, Q., & Zhang, X. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110301 · Cited by 124
Self-citationSustainable energies and machine learning: An organized review of recent applications and challenges
Ifaei, P., Nazari-Heris, M., Tayerani Charmchi, A. S., Asadi, S., & Yoo, C. (2023) · Energy · doi:10.1016/j.energy.2022.126432 · Cited by 79
Metadata
- Authors
- Zhao, Y., Zhang, C., Zhang, Y., Wang, Z., & Li, J.
- Venue
- Energy and Built Environment
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 1
- Issue
- 2
- Pages
- 149-164
- ISSN
- 2666-1233 (ISSN-L)
- OpenAlex
- W2988729496
- Cited by
- 303
- References
- 162
- FWCI
- 22.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. https://app.cochat.ai/papers/10.1016/j.enbenv.2019.11.003
- The paper
- Zhao, Y., Zhang, C., Zhang, Y., Wang, Z., & Li, J. (2020). A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. Energy and Built Environment. https://doi.org/10.1016/j.enbenv.2019.11.003
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.
