The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy
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Citations
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Cited by
A deep learning framework for building energy consumption forecast
Somu, N., Raman M R, G., & Ramamritham, K. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110591 · Cited by 500
Machine learning for estimation of building energy consumption and performance: a review
Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M. (2018) · Visualization in Engineering · doi:10.1186/s40327-018-0064-7 · Cited by 482
A data-driven predictive model of city-scale energy use in buildings
Kontokosta, C. E., & Tull, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.04.005 · Cited by 288
A hybrid model approach for forecasting future residential electricity consumption
Dong, B., Li, Z., Rahman, S. M. M., & Vega, R. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2015.09.033 · Cited by 229
Nutkiewicz, A., Yang, Z., & Jain, R. K. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.023 · Cited by 223
Self-citation
Metadata
- Authors
- Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 123
- Pages
- 168-178
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2064469609
- Cited by
- 600
- References
- 3
- FWCI
- 54.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. https://app.cochat.ai/papers/10.1016/j.apenergy.2014.02.057
- The paper
- Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy. https://doi.org/10.1016/j.apenergy.2014.02.057
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.
