The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks
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Citations
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
A review on buildings energy consumption information
Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008) · Energy and Buildings · doi:10.1016/j.enbuild.2007.03.007 · Cited by 6,649
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,388
Neural networks for short-term load forecasting: a review and evaluation
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001) · IEEE Transactions on Power Systems · doi:10.1109/59.910780 · Cited by 2,221
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
Gauss-Newton approximation to Bayesian learning
Dan Foresee, F., & Hagan, M. T. (2002) · Proceedings of International Conference on Neural Networks (ICNN'97) · doi:10.1109/icnn.1997.614194 · Cited by 1,390
Neural Networks: Algorithms, Applications and Programming Techniques
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Cited by
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
Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review
Runge, J., & Zmeureanu, R. (2019) · Energies · doi:10.3390/en12173254 · Cited by 268
Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
Fu, G. (2018) · Energy · doi:10.1016/j.energy.2018.01.180 · Cited by 181
Metadata
- Authors
- Deb, C., Eang, L. S., Yang, J., & Santamouris, M.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 121
- Pages
- 284-297
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2201221078
- Cited by
- 283
- References
- 33
- FWCI
- 24.34
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. https://app.cochat.ai/papers/10.1016/j.enbuild.2015.12.050
- The paper
- Deb, C., Eang, L. S., Yang, J., & Santamouris, M. (2016). Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2015.12.050
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.
