The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
A review and analysis of regression and machine learning models on commercial building electricity load forecasting
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178
Willmott, C., & Matsuura, K. (2005) · Climate Research · doi:10.3354/cr030079 · Cited by 6,512
Time Series Analysis and Its Applications: With R Examples
Shumway, R. H., & Stoffer, D. S. (2017) · Springer Texts in Statistics · doi:10.1007/978-3-319-52452-8 · Cited by 2,093
Time Series Analysis and Its Applications
Shumway, R. H., & Stoffer, D. S. (2011) · Springer Texts in Statistics · doi:10.1007/978-1-4419-7865-3 · Cited by 2,012
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
Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis
Morgan, M. G., & Henrion, M. (1990) · Cambridge University Press eBooks · doi:10.1017/cbo9780511840609 · Cited by 1,711
Neural Network Toolbox™ User's Guide
Beale, M. H., Hagan, M., & Demuth, H. B. (2015) · Cited by 1,643
Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Machine learning driven smart electric power systems: Current trends and new perspectives
Ibrahim, M. S., Dong, W., & Yang, Q. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115237 · Cited by 435
A comprehensive review on deep learning approaches for short-term load forecasting
Eren, Y., & Küçükdemiral, İ. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114031 · Cited by 303
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
Zhu, J., Dong, H., Zheng, W., Li, S., Huang, Y., & Xi, L. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119269 · Cited by 298
Farsi, B., Amayri, M., Bouguila, N., & Eicker, U. (2021) · IEEE Access · doi:10.1109/access.2021.3060290 · Cited by 292
Metadata
- Authors
- Yildiz, B., Bilbao, J. I., & Sproul, A. B.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 73
- Pages
- 1104-1122
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2586259521
- Cited by
- 560
- References
- 42
- FWCI
- 17.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2017.02.023
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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.
