The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Deep learning for estimating building energy consumption
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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 Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
Neural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Cited by
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104440 · Cited by 1,007
“Mocanu et al. [170] sought to use deep learning methods to predict a building's energy consumption through the application of Conditional Restricted Boltzmann Machines (CRBM) and Factored Conditional Restricted Boltzmann Machines (FCRBM) which they compared to traditional ANNs, SVMs and RNNs. Ma et…”
MethodBouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Metadata
- Authors
- Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L.
- Venue
- Sustainable Energy, Grids and Networks
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 6
- Pages
- 91-99
- ISSN
- 2352-4677 (ISSN-L)
- OpenAlex
- W2295959395
- Cited by
- 622
- References
- 6
- FWCI
- 22.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning for estimating building energy consumption. https://app.cochat.ai/papers/10.1016/j.segan.2016.02.005
- The paper
- Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks. https://doi.org/10.1016/j.segan.2016.02.005
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.
