The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Predicting future hourly residential electrical consumption: A machine learning case study
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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
Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
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 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
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
Huang, J., & Kaewunruen, S. (2023) · Energies · doi:10.3390/en16020966 · Cited by 38
Metadata
- Authors
- Edwards, R. E., New, J., & Parker, L. E.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 49
- Pages
- 591-603
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2130608062
- Cited by
- 391
- References
- 2
- FWCI
- 28.54
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Predicting future hourly residential electrical consumption: A machine learning case study. https://app.cochat.ai/papers/10.1016/j.enbuild.2012.03.010
- The paper
- Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2012.03.010
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.
