The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Solar forecasting methods for renewable energy integration
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Cites
Multilayer feedforward networks are universal approximators
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Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
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
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
The Kolmogorov-Smirnov Test for Goodness of Fit
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ELIZA—a computer program for the study of natural language communication between man and machine
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Numerical Methods for Fluid Dynamics
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Cited by
Machine learning methods for solar radiation forecasting: A review
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836
Solar photovoltaic generation forecasting methods: A review
Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.019 · Cited by 990
A review on the selected applications of forecasting models in renewable power systems
Ahmed, A., & Khalid, M. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.09.046 · Cited by 325
Serban, A. C., & Lytras, M. D. (2020) · IEEE Access · doi:10.1109/access.2020.2990123 · Cited by 300
PV power forecast using a nonparametric PV model
Almeida, M. P., Perpiñán, O., & Narvarte, L. (2015) · Solar Energy · doi:10.1016/j.solener.2015.03.006 · Cited by 208
Metadata
- Authors
- Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M.
- Venue
- Progress in Energy and Combustion Science
- Publisher
- Elsevier BV
- Published
- 2013-12
- Volume
- 39
- Issue
- 6
- Pages
- 535-576
- ISSN
- 0360-1285 (ISSN-L)
- OpenAlex
- W2067019903
- Cited by
- 1,062
- References
- 10
- FWCI
- 59.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Solar forecasting methods for renewable energy integration. https://app.cochat.ai/papers/10.1016/j.pecs.2013.06.002
- The paper
- Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science. https://doi.org/10.1016/j.pecs.2013.06.002
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.